THE ISBA BULLETIN

Vol. 21 No. 4 December 2014

The o�cial bulletin of the International Society for Bayesian Analysis

A MESSAGE FROM THE PRESIDENT

- Sonia Petrone -ISBA President, 2014

sonia.petrone@unibocconi.it

Last summer, at a conference, I was discussingwith Wilfrid Kendall, the President of Bernoulli,about possible joint initiatives between ISBA andthe Bernoulli Society. He asked me for how longone serves as President at ISBA. I said it’s oneyear. Smile and reply: “I know, that’s the life of abutterfly! so short!” Short? I can assure you, thelife of a butterfly is quite demanding! I cannotdeny a certain relief now that my year of Presi-dency is coming to an end! Many initiatives havebeen going on this year at ISBA, many things totake care of. But speaking seriously, this is great, asign of the liveliness of the Society! Bayesian Sta-tistics is more and more relevant in many fields,and ISBA wonderfully reflects this spread of inte-rests, applications, research, in the lively growingBayesian community. My sincere thanks to all thepeople that generously contributed to ISBA acti-vities this year, to the ISBA officers, the editorialBoards of our publications, the Committees, to allof you for supporting ISBA!

But before I keep on thanking you, let me takeyou on a butterfly-flight over some ongoing initia-tives and objectives.

• We are proud that the ISBA official journal,Bayesian Analysis, continues to grow in pre-stige and diffusion, thanks to your contri-butions and the work of the editorial Boardand of the Editor in Chief, Marina Vannucci.I’d like to underline here that we continue tooffer online open-access of Bayesian Analy-sis, through ProjectEuclid, despite the costs.This is something we all should be proud tosupport - starting by joining ISBA!

• ISBA sponsors, co-sponsors and endorsesmany meetings around the world. As youknow, we usually focus on the ISBA WorldMeeting in even years, and on differentworkshops on odd years. The World Mee-ting 2014 in Cancun was a great success,for which I would like to thank again AndresChristen and the Local Organizing Commit-tee, and the Scientific Committee, chairedby Raquel Prado. Next year will bring ma-ny ISBA endorsed workshops, including theofficial meetings of the ISBA sections onObjective Bayes and Bayesian Nonparame-trics, ISBA Chapters’ meetings, and the 9thWorkshop on Bayesian Inference in Stocha-stic Processes, BISP9.

In this issue

‰ FROM THE PROGRAM COUNCIL*Page 4

‰ ISBA 2016 & 2018*Page 5

‰ A MESSAGE FROM THE BA EDITOR*Page 6

‰ ISBA@NIPS WORKSHOP*Page 6

‰ OBITUARY: KATHRYN M. CHALONER*Page 7

‰ BAYESIAN INFERENCE & BIG DATA*Page 8

‰ STUDENTS’ CORNER*Page 12

‰ NEWS FROM THE WORLD

*Page 15

ISBA Bulletin, 21(4), December 2014 A MESSAGE FROM THE PRESIDENT

MESSAGE FROM THE PRESIDENT, Continuedfrom page 1. . . .

More information at http://bayesian.

org/meetings/ISBA-meetings.With the growth of the Society, the ISBAWorld Meeting has become quite a big con-ference. The latest World Meeting, in Can-cun, last July, had more than 400 partici-pants. Sustaining this growth, while kee-ping something of the spirit of the Valen-cia meetings, the passion for sharing deepideas and discussion, the sense of commu-nity, is an important challenge. We discus-sed about encouraging some special plena-ry moments at the World Meeting, and newinitiatives will be reported in the next is-sue of the Bulletin. I will anticipate only onething. This has been a sad autumn. We hadlost Susie Bayarri, and last October we lostanother great Bayesian statistician, KathrynChaloner. We will present, at the ISBA WorldMeeting 2016, initiatives to remember andhonor Susie and Kathryn.

• Support to young researchers is a co-re objective for ISBA. ISBA initiatives foryoung researchers include prizes and nume-rous travel awards. The prestigious Sava-ge award, co-sponsored by ISBA and by theASA Section on Bayesian Statistical Science,SBSS, is assigned every year for outstan-ding doctoral dissertations in Bayesian theo-ry and applications. The high quality of theapplications is an exciting sign of the grow-th of Bayesian Statistics. I thank the 2014Savage Committee, chaired by Judith Rous-seau, for their work on the selections for the2014 Savage Award.ISBA funds a generous number of TravelAwards for the World Meeting, and ISBAco-sponsorship of endorsed workshops isusually finalized to travel support for stu-dents and new researchers. A current objec-tive is to give more visibility to ISBA Tra-vel Awards, starting from proper announce-ment of the recipients in the Bulletin and atthe meetings. Moreover, for a better plan-ning, we are fixing a yearly deadline forapplying for ISBA endorsement and tra-vel support. Details will be promptly pro-vided on the website, http://bayesian.

org/meetings/planning, and in the Bulle-tin.

We unfortunately cannot afford yet to gi-ve free student membership. However, afirst step has been taken, by acknowledgingone free student membership for each ISBAmember in countries with reduced subscrip-tion fees, starting from 2015.A challenge that students and new resear-chers are facing, possibly more than others,is the impact of new technologies and bigdata - including their impact on the re-cruitment policies of many Statistics depart-ments. As a Society, caring for such oppor-tunities and challenges is unavoidable; and Ibelieve that being open is good. The ISBA atNIPS initiative this year - reported in the lastissue of the Bulletin - has been a first step.I also would like to thank Max Welling andYee-Whye Teh for having taken on my callfor a discussion in the Bulletin (with pos-sible developments on Bayesian Analysis?),contributing a note – in this issue – aboutthe First Workshop on Bayesian Inferencewith Big Data, that took place in Amsterdamlast August.

• Enhancing collaborations with other So-cieties and supporting our profession areongoing objectives. ISBA is a member ofthe Friends of COPSS. We encourage in-teraction of the junior section of ISBA,J-ISBA, with other young research com-munities and initiatives, such as the Ber-noulli European Young Statisticians Mee-ting and the IMS Meeting of New Rese-archers in Statistics and Probability. Lastyear, Kerrie Mengersen was elected as theISBA representative in the World of Stati-stics initiative. The latest news can be foundat http://www.worldofstatistics.org/

files/2014/12/December-9-2014.pdf.Let’s all step in, communicate initiatives toKerrie, and make Bayesian events more andmore visible in the World of Statistics!

• A Continuing Education committee wascreated in 2012, to promote and coordina-te short courses, webinars and other acti-vities. These include the ISBA Latin Ame-rican School of Bayesian Statistics, whichaims at the dissemination of Bayesian me-thods and the promotion of an interactionbetween young statisticians in Latin Ameri-ca. Work is still needed to improve and fi-nalize web-based Continuing Education in-itiatives, but I forecast a lot of energy co-

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ISBA Bulletin, 21(4), December 2014 A MESSAGE FROM THE PRESIDENT

ming in from our new President, AlexandraSchmidt!

Many people have been doing fundamentalwork for the ISBA activities, and I thank them all.

The first, very special thanks go to our Past Pre-sident, Merlise Clyde. Merlise has given to ISBAthe most generous and extraordinary service, asthe ISBA Executive Secretary in 2009-2011, andas an ISBA President in 2012-2014. Merlise issimply amazing. As the ISBA Executive Secretary,she has greatly contributed to significant impro-vements of the ISBA website, which now providesa wide range of crucial and ever-growing servicesto the Bayesian community. Merlise is “an ama-zing talent – wrote Mike Jordan in his presidentialmessage in December 2011 - someone who is athome in the Web 2.0 world as she is in Bayesi-an theory and practice. After putting the kids tobed she appears to enjoy unwinding with a bitof Drupal and CiviCRM programming.” I can tellthat Merlise has been an even more wonderfulPresident! She has a vision of ISBA life as no-body else has. Her advise in the ISBA ExecutiveBoard has been fundamental. And she has ge-nerously continued to offer her expertise, to beour website-Drupal-civiCRM magician, not onlyafter putting the kids to bed, but also while beingthe Head of the Statistics Department at DukeUniversity. We will never thank you enough foryour support and advise, Merlise!

It has been a busy year, but a pleasure to workwith a wonderful Board of Directors. I thank SteveScott for his efficient, wise, prompt collaboration- and hilarious emails - as the ISBA Executive Se-cretary. Murali Haran, for starting his role as theISBA Treasurer in the busy year of the World Mee-ting, and for surviving the difficulties - includingthe 1.45 hours driving to initially set the bankaccounts - with kindness and calm, leading ISBAfinances in excellent shape. A special thank youto Mike Daniels, who kindly accepted to serve as

the ISBA “Past Treasurer” in the Finance Commit-tee, after being the ISBA Treasurer for 3 years.Having the “ Past Treasurer” overlapping one yearwith the Treasurer has been greatly helpful andwe plan to formalize such rotation in the future.

I am grateful to the ISBA Program Council,Ramses Mena, Michele Guindani and Raquel Pra-do - the scientific leaders of ISBA! I thank themembers of the ISBA Board of Directors that willcomplete their mandate at the end of 2014: KateCalder, Maria de Iorio, Igor Pruenster and WesJohnson. A double thank you to Wes, who alsowisely and efficiently served as the Chair of theISBA Prize Committee in 2014. Thank you to JimBerger for chairing the Committee on Fellows onits first year of activity, and to all the componentsof ISBA Committees for their precious work.

Last but not least, my sincere and gratefulthank you goes to GabriellaBonfanti, who started to col-laborate with ISBA as Admi-nistrative Manager this year,and is giving precious sup-port to ISBA officers. I couldeventually steel and pass toFeng a picture of her to bepublished in the Bulletin, soyou can all get to know her. Thank you Gabriellafor your collaboration.

I am delighted to welcome the ISBA Presi-dent Elect, Steve McEachern, and the new electedmembers of the Board, Sudipto Banerjee, CarlosM. Carvalho, Vanja Dukic and Alessandra Gugliel-mi, and all the new elected Sections’ officers. Ileave ISBA in good hands: to the Brazilian ener-gy and German efficiency of the ISBA PresidentAlexandra Schmidt.

My warmest wishes of a splendid 2015 to all ofyou! s

A MESSAGE FROM THE EDITOR

- Feng Liang -liangf@illinois.edu

Another great and fascinating year has passedaway. As being summarized in the last messagefrom our president, many activities and many in-

itiatives have been going on in our society thisyear. As the Editor of the Bulletin, I would liketo thank Sonia for all her support and guidance;as a member of ISBA, I would like to thank herfor taking us on such an exciting yet gracefulbutterfly-ride in the year of 2014!

I also want to take this opportunity to expressmy sincere gratitude to Isadora Antoniano and

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ISBA Bulletin, 21(4), December 2014 FROM THE PROGRAM COUNCIL

Antonio Ortiz (the Associate Co-Editors of Stu-dents’ Corner), Jarad Niemi (who’s in charge ofthe web publishing of the Bulletin), and contri-butors like members from our Program Council,Section Chairs, Editor-in-Chief of BA, and manyothers. Thank you for all your help and support!

Big Data has started to affect our everyday livesin many ways, including the life of Mickey Mouse.I just got back from a trip to Disney World at Or-lando. This is the first year Disney’s FastPass goesinto a paperless online system, and all the plan-ning needs to be done at “My Disney Experience,”an online vacation-planning system provided byWalt Disney. Every day hundreds and thousandsbytes of data ranging from personal informati-on to waiting time for a popular ride have beencollected by this system from millions of Disneyvisitors, waiting to be analyzed by data scientists.On the one hand, it is good to know that opportu-nities for us statisticians in the era of Big Data areenormous, but on the other hand, we should beaware of the challenges that come with Big Data.That is what you will find out in several articles

in this issue, including a special report on the firstworkshop on Bayesian Inference with Big Data.

In this December issue, you’ll also find an upda-te from our Editor-in-Chief of BA, announcementson ISBA World Meetings in 2016 and 2018 fromour Program Council, reports on ISBA endorsedworkshops, and other news in our society. In theStudents’ Corner Section, Julien Cornebise, a for-mer Savage Award winner, is sharing with us hispost-graduation experience, and a group of blog-gers is discussing the pros and cons on bloggingas young researchers.

Before closing my message, I want to sadlybring your attention to the Obituary for KathrynChaloner, another great Bayesian statistician welost in 2014.

Last, but not least, a word of thanks to YOU, de-ar readers of the Bulletin! In order for the Bulletinto become better, we need the help from YOU, thereaders, and the best way to help us is to email usyour suggestions/contributions in 2015! s

FROM THE PROGRAM COUNCIL

ISBA AT NIPS

- Raquel Prado, Ramses H. Mena andMichele Guindani -

ramses@sigma.iimas.unam.mx

The ISBA Program Council is pleased to reporton the ISBA@NIPS Travel Awards. These specialTravel Awards were created as part of an ISBAinitiative aimed at highlighting the importanceand impact of Bayesian methods in the new eraof data science. As part of such an initiative fourBayesian workshops at NIPS were granted withthe ISBA endorsement (see the previous numberof this Bulletin). The ISBA@NIPS Travel Awardswere especially designed to recognize the work oftwo young participants of ISBA-endorsed work-shops at NIPS, one in the category of InvitedPaper and one in the category of ContributedPaper.

After carefully reviewing all applications the IS-BA Program Council designated David Knowles asthe awardee in the category of Invited Paper (seereport in the previous number of this Bulletin)and Dennis Prangle, Richard Rado postdoctoralfellow in Statistics at the University of Reading,as the winner in the Contributed Paper Category.Dr. Prangle has done important interdisciplinarycontributions in the development of advanced ap-proximate Bayesian computation (ABC) methods.The ISBA Program Council greatly acknowledgeshis work and congratulates him with this award,as part of the ISBA@NIPS initiative.

As it can be seen from the report by TamaraBroderick [see page 6], co-organizer of the ISBAendorsed workshop at NIPS –Advances in Varia-tional Inference–, the endorsed workshops werea success within NIPS. ISBA is happy to see thatthese first actions of the ISBA@NIPS initiative ha-ve been of the interest of both of our academiccommunities. s

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ISBA Bulletin, 21(4), December 2014 WORLD MEETINGS 2016 AND 2018

WORLD MEETINGS 2016 AND 2018

- Michele Guindani -on behalf of the Program Council of ISBA

micheleguindani@gmail.com

The ISBA World Meetings are the continuation ofthe traditional Valencia/ISBA Meetings held regu-larly since 1979. They represent a unique eventwhere the Bayesian community gathers togetherto discuss recent advances and the future of ourprofession, at the same time looking back to ourroots and traditions, following the footsteps ofthose who laid the foundations of where we arenow. The ISBA World Meetings are an opportunityfor young, senior and “giant” statisticians to meetand talk to each other in an environment thatbreaks up commonly held barriers and promotesthe free flow of ideas, respectful confrontations,and ever-lasting collaborations. Someone in mar-keting would say that the ISBA meeting is theplace to be with the people who matter! Surelyenough, many of us think that the Bayesian com-munity would not be the same today without firstthe Valencia meetings and now the ISBA Worldmeetings. This is why the next two editions ofthe ISBA World Meetings will be held in locationswhere this continuous link between past and fu-ture will be most tangible.

The ISBA 2016 World Meeting will be held inItaly, from June 13th to June 17th, 2016. Thischoice will definitely present an opportunity toacknowledge the role of Bruno de Finetti in mo-dern Bayesian thought - the 30th anniversary ofhis death will be widely celebrated in 2015 bythe Italian mathematical, probability, actuarialand, of course, statistical communities. Thanks tode Finetti and his disciples, Bayesian ideas andmethods have been lively encouraged and dis-seminated in Italy during the last century. Themeeting will be held in the beautiful region ofSardinia, the second largest island in the Medi-terranean Sea, and one of the most geologicallyancient bodies of land in Europe. Due to its positi-on at the heart of the Mediterranean Sea, Sardiniaboasts old traditions and a wild and pure lands-cape. Hundreds of nuraghe (stone buildings) arescattered through the landscape, testifying to an

ancient culture that endured there from the 16thto 15th Centuries B.C. The mountains are hometo a wide variety of rare or uncommon animalsand plants. The seaside landscapes are conside-red among the most beautiful in the world. TheLocal Organizing Committee of the conference iscurrently finalizing the negotiations with a lea-ding conference venue and hotel seaside resortin the south of Sardinia, to secure the best pricesand conditions for the attendees. The Local Orga-nizing Committee is composed of Stefano Cabras(chair), Walter Racugno, Monica Musio, Brune-ro Liseo, Igor Pruenster, Antonio Lijoi and LauraVentura. Plans are also well under way to definethe scientific program of the meeting, which willconform to the usual high standards of the ISBAWorld meetings. For example, the traditional fea-ture poster presentation will be the well-attendedcenter point of the nights of Tuesday, Wednesdayand Thursday nights. The scientific committee hasbeen recently formed and includes ISBA memberswho are representative of the various “soulsandthe diverse geography of the society. Short cour-ses are planned on June 12th on the premisesof the nearby University of Cagliari. Updates onthe program, registration fees, accommodation,and VISA requirements, will be provided soon,as usual, on the bayesian.org website, the ISBAmailing lists and on a dedicated website for theconference.

The ISBA 2018 World Meeting will be held onthe premises of University of Edinburgh, Scot-land, UK. This will represent a return to the orig-ins for the Bayesian community, as the very samerev. Thomas Bayes was a student of the Universityof Edinburgh from 1719 to c. 1722. Edinburgh isthe capital of Scotland, and is rich of historicaland cultural attractions. For example, Edinbur-gh’s Old Town and New Town are jointly listed asa UNESCO World Heritage Site. According to theplan of the local organizing committee, the confe-rence should be held in central Edinburgh, at theAppleton Tower (and Informatics Forum) whichare located at the central campus of the Universityof Edinburgh. The local organizing committee iscomposed of Chris Holmes (chair), Natalia Boch-

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ISBA Bulletin, 21(4), December 2014 BAYESIAN ANALYSIS - A MESSAGE FROM THE EDITOR

kina, Mark Girolami, Judith Rousseau and ChrisWilliams. Of course, more details will be providedas we approach the conference.

Finally, a special thanks to those members ofour community who have contributed proposals

to organize the World Meetings in the past, and anencouragement to contribute proposals for futureISBA meetings, according to the schedule andcalls available on the ISBA website. It is thanks tothe generosity of our members that ISBA is pro-spering as a community and as a society. s

BAYESIAN ANALYSIS - A MESSAGE FROM THE EDITOR

UPDATE FROM BA- Marina Vannucci -

Editor-in-Chief

marina@rice.edu

The December issue of Bayesian Analysis featu-res a discussion paper by Jesse Windle and CarlosM. Carvalho titled “A Tractable State-Space Modelfor Symmetric Positive-Definite Matrices”. In thispaper the authors present a state-space modelwhose observations and latent states take valueson the manifold of symmetric positive-definitematrices and for which one may easily compu-te the posterior distribution of the latent statesand the systems parameters as well as filtereddistributions and one-step ahead predictions. Em-ploying the model within the context of finance,they show how one can use realized covariance

matrices as data to predict latent time-varyingcovariance matrices. This approach out-performsfactor stochastic volatility. The manuscript is pu-blished together with three invited discussions,followed by a rejoinder. The issue also containsother fine articles on various topics of Bayesianstatistics.

We remind readers that at BA it is now possi-ble for individual authors to submit manuscriptsfor consideration as discussion papers. Such sub-missions will first go through our regular reviewprocess and, if accepted, the Editor in charge andthe EiC will make a decision as to whether themanuscript can make a good discussion paper. Ifyou wish to submit your work for consideration,please select “Article with Discussion” when sub-mitting at EJMS. s

ISBA@NIPS WORKSHOP

NIPS 2014 WORKSHOP ONADVANCES IN VARIATIONAL

INFERENCE

- Tamara Broderick -tab@stat.berkeley.edu

I am excited to be able to report on the greatsuccess of the ISBA@NIPS initiative this year asa co-organizer of one of the ISBA-endorsed work-shops at the 2014 Neural Information ProcessingSystems conference. In particular, I was privilegedto organize the NIPS 2014 Workshop on Advancesin Variational Inference together with Shakir Mo-

hamed, Charles Blundell, Matt Hoffman, DavidBlei, and Michael I. Jordan. We had an amazingturnout, with over 150 attendees packed in to alarge room at the Palais des congres de Montreal.Throughout most of the day-long workshop onSaturday, December 13, even the standing roomin the back was full as we discussed challengesand advances in variational inference.

Variational inference has been enjoying a re-cent resurgence in interest. The ever-increasingsize of data sets—e.g., in large-scale scientificand industrial applications—calls for a focus onscalability in Bayesian inference. Traditional sche-mes for posterior approximation such as Markov

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ISBA Bulletin, 21(4), December 2014 OBITUARY: KATHRYN M. CHALONER (1954-2014)

chain Monte Carlo (MCMC) can be slow to runand difficult to evaluate in finite time. By contrast,variational inference allows for competitive runtimes and more reliable convergence diagnosticson large-scale and streaming data. The recentfocus on such data sets has led to a booming in-terest in variational methods. But there remainsignificant trade-offs in speed, accuracy, simpli-city, applicability, and learned-model complexitybetween variational inference and other approxi-mative schemes such as MCMC and point estima-tion. Our workshop brought together researchersand practitioners to highlight recent advances invariational inference, to identify areas for impro-vement in current methodologies and software,and to discuss future directions and solutions tothese issues.

Our workshop captured the groundswell of in-terest and current research in variational me-thods; the event included 6 invited talks, 4 con-tributed talks, 6 spotlight talks, and 34 total ac-cepted posters. The presentations covered a greatdiversity of advances within variational inference,including but not limited to: the location of glo-bal vs. local optima in the variational optimizati-on problem, non-conjugate models, nonparame-tric models, streaming data, accurate uncertain-ty estimation, stochastic optimization, theoreti-cal guarantees, integration of variational methodswith MCMC, and a variety of applications. We fi-nished the day with a rousing panel discussionfeaturing a line-up of leading experts in the field:David Blei, Zoubin Ghahramani, Neil Lawrence,Shinichi Nakajima, and Matthias Seeger. The fullworkshop schedule, talk slides, and poster PDFsare available at our workshop website:

http://variationalinference.org.

Abbildung 1: The workshop in progress.

Abbildung 2: The workshop panel. Left to right:Shinichi Nakajima, Zoubin Ghahramani, DavidBlei, Neil Lawrence, Matthias Seeger.

We are indebted to ISBA not only for its endor-sement and for forming the ISBA@NIPS initiati-ve, but also for awarding the ISBA@NIPS SpecialTravel Award in the category of invited speakerto one of our speakers: David Knowles. We arefurther grateful to Google DeepMind and AdobeResearch for additional workshop funding, to ourlarge program committee for their thoughtful re-views, and to all of the workshop participants.s

OBITUARY: KATHRYN M. CHALONER (1954-2014)

The world lost a great Bayesian statistician andan amazingly gracious and wonderful person onOctober 19, 2014. Kathryn Chaloner died aftera long and courageous fight against cancer. Shewas 60 years old and much, much too young toleave us. Kathryn is survived by her husband, Lu-ke Tierney who is also a world-class statistician,and her two sons, Graham and Patrick.

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ISBA Bulletin, 21(4), December 2014 INVITED CONTRIBUTION

At the time of her death Kathryn was Profes-sor and Head of the Department of Biostatisticsat the University of Iowa in the College of PublicHealth. The department was established in 1999and she became its Head in 2002 when she andLuke moved to Iowa from Minnesota. She pro-ceeded to build a first-class department workingclosely with Iowa’s Department of Statistics andActuarial Science.

Kathryn’s PhD degree in 1982 was fromCarnegie-Mellon where her advisor was MorrisDeGroot. The focus of much of her research wasBayesian. She made important contributions inBayesian inference, decision theory, and desi-gning experiments, including clinical trials. ButKathryn was different from most Bayesians inthat she applied her work and that of others to re-al problems, especially in research involving HIVand other infectious diseases. For example, shewas a widely acknowledged expert in the elicita-tion of subjective probabilities. But as opposed toexperts in how it should be done, she actually didit!

I was at the University of Minnesota in 1982when Kathryn joined the faculty. We had similarbackgrounds and interests. I had then and I conti-nued to have the greatest respect for her thinkingand her research. She was brilliant. But she wasalso nice to be around. She and I worked togetherin editing (with John Geweke) a festschrift hono-ring Arnold Zellner. This volume owes much toher good judgment. She had a marvelous intuiti-on for what research is relevant in this world andwhat will wither on the vine.

When Telba Irony and I set up an annual con-ference on Bayesian Biostatistics in Houston we

naturally asked Kathryn to be on the ProgramCommittee. Again, we benefited and the confe-rence benefited greatly from her good judgment.

The professional aspects of the Kathryn Cha-loner story center on teachingNor rather, on stu-dents. The distinction is important. She was agreat mentor and role model. She respected stu-dents and they respected her. Her students attestto this with tributes to her on the internet. Forexample, https://faculty.biostat.ucla.edu/robweiss/taxonomy/term/105.

As a final acknowledgment of her great workwith students, especially women students, shewas awarded the 2014 Elizabeth L. Scott Awardby the Committee of Presidents of Statistical So-cieties (COPSS) just two months before her death.Her citation reads:“For her commitment and suc-cess in developing programs to encourage and fa-cilitate women to undertake careers in statistics;for extensive mentoring of women students andyoung faculty; for work to identify and removeinequities in employment for under-representedcomponents of the profession; and for serving asa role model, balancing work and family whileexcelling as a teacher, researcher and academicadministrator.”

A memorial fund has been established in honorof Kathryn through the University of Iowa Foun-dation to support underrepresented minority stu-dents in the College of Public Health Departmentof Biostatistics. Contributions should be sent to:Kathryn Chaloner Student Scholarship Fund, UIFoundation, P.O. Box 4550, Iowa City, IA 52244-4550.

Donald BerryHouston, Texas

INVITED CONTRIBUTION

BAYESIAN INFERENCE & BIGDATA: A SNAPSHOT FROM A

WORKSHOP

- Max Welling, Yee Whye Teh, ChristopheAndrieu, Jakub Kominiarczuk,Ted Meeds,

Babak Shahbaba, & Sebastian Vollmer -

welling.max@gmail.com

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ISBA Bulletin, 21(4), December 2014 INVITED CONTRIBUTION

Bayesian Inference in the Age of BigData

Over the last half century, and particularly sin-ce the advent of Markov Chain Monte Carlo me-thods, Bayesian inference has enjoyed tremen-dous successes, so much so that some quartershave proclaimed the age-old philosophical argu-ments between Frequentism and Bayesianism re-solved, and that the twenty-first century is theBayesian century. Whether such proclamationsare controversial or not, the twenty first centu-ry has brought a new challenge to Bayesian in-ference that may render moot such philosophicalarguments without a serious discussion of compu-tation. This is the challenge of Big Data.

Indeed, the growth in volume and variety of da-ta in the twenty first century has been staggering,and one could ask the question whether Bayesianinference is still a relevant statistical frameworkin this context. Does one still have to worry aboutmodel uncertainty and overfitting if there is somuch data that uncertainties are negligible for allpractical purposes? We believe there are a num-ber of responses to this critique that rather pointto the opposite direction.

Firstly, big data often means large p with p�N . For instance, modern MRI scanners record mil-lions of voxels, possibly over time, for a handfulof patients only. For another example, the cost forsequencing entire genomes is falling faster thanMoore’s law, implying that within a few years adoctor could routinely sequence every patient’sgenome: this is four billion basepairs per patient.Clearly, model uncertainty remains an importantproblem. Secondly, most of the data may not berelevant to certain model components. For instan-ce, in recommendation systems with billions ofuser-product interactions, almost all of this datais irrelevant for any new user or product enteringthe system: this is the so-called cold start problem.Lastly, with the recent advances in deep learning,the number of parameters of modern neural net-work models have been growing at a faster pacethan data volumes, with tens of billions parame-ters been reported by large internet firms such asGoogle, Yahoo!, Facebook and Baidu. Add to thisthe fact that the amount of “useful” or predicti-ve information in data usually grows sublinearlyin N . We thus observe that model capacity canin fact be growing faster than the amount of in-

formation we would like to store in it. That over-fitting can still be a serious issue in this domainis evidenced by the fact that a new, rather extre-me form of regularization (dropout) was inventedto prevent it in neural networks (9). One couldargue that any model where regularization is nota consideration because the model’s capacity issmall compared to the data volume is in fact un-derfitting and therefore not optimally predictive(unless the data was generated from the model,which is never the case in reality).

What then are the challenges that a Bayesianapproach faces in a Big Data context? Not sur-prisingly, a major challenge is computation. Tra-ditionally two approaches exist to Bayesian infe-rence: MCMC sampling and variational optimiza-tion. The former is asymptotically unbiased butoften suffers from a significant variance in its esti-mates while the second is deterministic and hasno variance but often exhibits rather strong bia-ses. Until recently, standard coordinate ascent im-plementations of both methods were unsuited forvery large datasets because every update (samp-ling or variational) depended on all data items1.

So what criteria does an inference method needto satisfy in order to be suitable for Big Data? Forcases where big data means large N , we propo-se the following desiderata: Every update needsto only access a small subset of the whole data-set. Moreover, the size of this subset should notdepend on the size of the whole dataset, in otherwords, the algorithm should still make progress ifthe dataset is infinitely large (i.e. streaming da-ta). Further, the algorithms should be amenableto execution on distributed systems using, say, theMapReduce framework.

Fortunately, important progress is being madeon both desiderata. Stochastic variational updateswere first reported for Latent Dirichlet Allocationin (12) which have since then been extended to awide class of models (6; 13; 20; 19; 11; 10; 24).Similarly, the first stochastic subset MCMC algo-rithm was reported in (26), and since then al-so extended in multiple directions (1; 14; 4; 5).Good progress has also been made in terms of dis-tributed implementations of both variational andMCMC methods (21; 25; 18; 3; 2).

1Efficient implementations of variational inference algorithms did however exist in the form of message passing algorithms(27; 17) while efficient MCMC updates can also be achieved in some models by collapsing over the parameters (7). (We thankTom Minka for pointing this out.)

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ISBA Bulletin, 21(4), December 2014 INVITED CONTRIBUTION

First Workshop on Bayesian Inferencewith Big Data (BIBiD)

On August 28 and 29, 2014, the authors, partici-pants in a NSF/EPSRC joint funded project, con-vened the First Workshop on Bayesian Inferencewith Big Data (BIBiD’14) at the University of Am-sterdam to discuss these issues and to present theinitial results of our current research.

Sebastian Vollmer presented a convergenceproof for the stochastic gradient Langevin dyna-mics (SGLD) algorithm first presented in (26),along with showing that its rate of convergence israther slow at O(N�1/3) (23). New variants andalternative analyses were also discussed whichshed light on the non-asymptotic behaviour of thealgorithms.

Babak Shahbaba presented ideas for improvingBig Data MCMC by exploiting the Riemanniangeometry of the posterior. One idea is to split theproblem into “easy” and “hard” parts, and usingHamiltonian Monte Carlo (HMC) with varyingstep-sizes to reduce computation on the “hard”parts (22). Another idea, for models over a cons-trained space, is to map the space onto a sphereand use Riemannian HMC on the sphere for effi-cient sampling (15).

Ted Meeds presented a more efficient ABCMCMC sampler for simulation based (likelihoodfree) models (16). In approximate Bayesian Com-putation (ABC) the typical scenario is that the li-kelihood can only be probed through a complexand expensive simulation. The proposed methodlearns a surrogate functions of the likelihood du-ring runtime, significantly reducing the numbernecessary simulations.

Jakub Kominiarczuk discussed a Gibbs samplerthat depends only on stochastically chosen mini-batches. This led to a challenging problem, due tothe need to remove estimation bias from the naiveestimates of conditional probabilities.

Yee Whye Teh addressed the problem of distri-buted MCMC sampling (28). While parallel sche-mes can be easily implemented, they do either notexchange valuable information among the chains(embarassingly parallel) or use too much band-width exchanging information. The proposed me-thod involves each machine running a Markovchain, and asynchronously exchanging a limitednumber of sufficient statistics among the machi-nes. The algorithm can be interpreted as expecta-tion propagation where the moments are compu-ted using MCMC sampling.

Finally, Max Welling discussed variational Baye-

sian autoencoders (13) (VBA). VBAs are similarto Helmholtz machines (8) in their architecturebut use a more principled and scalable variationalBayesian learning algorithm to train both genera-tive and discriminative networks.

In conclusion, the BIBiD’14 attendees agreedthat Bayesian methods are both necessary andpossible in the context of very large datasets. Re-search on the fertile interface among statistics,machine learning and computation are necessaryto make Bayesian inference for Big Data a rea-lity. Two main classes of Big Data Bayesian in-ference methods have emerged in recent years:low variance but biased variational methods andunbiased but high variance MCMC methods. Ho-wever, the gap between these methods seems tobe closing fast since the expressive power of va-riational methods is rapidly improving (reducingbias) while the efficiency of MCMC sampling isalso rapidly increasing (reducing variance by thefact that more samples can be drawn). Of course,much work remains to be done in order to fur-ther improve the efficiency and accuracy of bothvariational methods and MCMC samplers.

We felt that the workshop has been a great ve-nue for informally exchanging our ideas on Baye-sian inference for Big Data. A future workshopwelcoming a larger slice of the Bayesian commu-nity may help to crystallize further work on ma-king the Bayesian framework relevant in the timeof Big Data.

References

[1] S. Ahn, A. Korattikara, and M. Welling.Bayesian posterior sampling via stochasticgradient Fisher scoring. In Proceedings of theInternational Conference on Machine Lear-ning, 2012.

[2] S. Ahn, B. Shahbaba, and M. Welling. Distri-buted stochastic gradient MCMC. In Procee-dings of the International Conference on Ma-chine Learning, 2014.

[3] E. Angelino, E. Kohler, A. Waterland,M. Seltzer, and R. P. Adams. AcceleratingMCMC via parallel predictive prefetching.In Proceedings of the International Confe-rence on Uncertainty in Artificial Intelligence,2014.

[4] R. Bardenet, A. Doucet, and C. Holmes. To-wards scaling up MCMC: An adaptive sub-

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sampling approach. In Proceedings of the In-ternational Conference on Machine Learning,2014.

[5] T. Chen, E. B. Fox, and C. Guestrin. Sto-chastic gradient Hamiltonian Monte Carlo.arxiv:1402.4102v1, 2014.

[6] A. Graves. Practical variational inferencefor neural networks. In Advances in NeuralInformation Processing Systems, pp. 2348-2356, 2011.

[7] T.L. Griffiths and M. Steyvers. Finding scien-tific topics. Proceedings of the National aca-demy of Sciences of the United States of Ame-rica 101.Suppl. 1 5228-5235, 2004.

[8] The wake-sleep algorithm for unsupervisedNeural Networks. Science, 268, pp. 1158-1161, 1995.

[9] G. E. Hinton, N. Srivastava, A. Krizhevsky,I. Sutskever, and R. R. Salakhutdinov. Im-proving neural networks by preventing co-adaptation of feature detectors. arXiv pre-print arXiv:1207.0580, 2012.

[10] M. Hoffman. Stochastic Structured Mean-Field Variational Inference. arXiv preprintarXiv:1404.4114, 2014.

[11] M. Hoffman, D. Blei, J. Paisley, and C. Wang.Stochastic variational inference. Journal ofMachine Learning Research, 14:1303–1347,2013.

[12] M. D. Hoffman, D. M. Blei, and F. Bach. On-line learning for latent dirichlet allocation.In Advances in Neural Information ProcessingSystems, 2010.

[13] D.P. Kingma and Max Welling. Auto-encoding variational bayes. InternationalConference on Learning Representations,2013. arXiv preprint arXiv:1312.6114.

[14] A. Korattikara, Y. Chen, and M. Wel-ling. Austerity in MCMC land: Cutting theMetropolis-Hastings budget. In Proceedingsof the International Conference on MachineLearning, 2014.

[15] S. Lan, B. Zhou, and B. Shahbaba. SphericalHamiltonian Monte Carlo for constrainedtarget distributions. In Proceedings of the In-ternational Conference on Machine Learning,2014.

[16] T. Meeds and M. Welling. GPS-ABC: Gaussi-an process surrogate approximate Bayesian

computation. In Proceedings of the Interna-tional Conference on Uncertainty in ArtificialIntelligence, 2014.

[17] T. Minka. Expectation propagation for ap-proximate Bayesian inference. In Procee-dings of the Seventeenth conference on Uncer-tainty in artificial intelligence, 2001.

[18] W. Neiswanger, C. Wang, and E. P. Xing.Asymptotically exact, embarrassingly paral-lel MCMC. In Proceedings of the Internatio-nal Conference on Uncertainty in Artificial In-telligence, 2014.

[19] R. Ranganath, S. Gerrish, and D.M. Blei.Black box variational inference. arXiv pre-print arXiv:1401.0118, 2013.

[20] T. Salimans and D.A. Knowles. Fixed-formvariational posterior approximation throughstochastic linear regression. Bayesian Ana-lysis 8, 837-882, 2013.

[21] S. L. Scott, A. W. Blocker, F. V. Bonassi,H. Chipman, E. George, and R. McCulloch.Bayes and big data: the consensus MonteCarlo algorithm. In Bayes 250, 2013.

[22] B. Shahbaba, S. Lan, W. O. Johnson, andR. M. Neal. Split Hamiltonian Monte Car-lo. Statistics and Computing, 2014.

[23] Y. W. Teh, A. H. Thiery, and S. J. Voll-mer. Consistency and fluctuations for sto-chastic gradient Langevin dynamics. submit-ted, 2014.

[24] M. Titsias and M. LA¡zaro-Gredilla. Doub-ly Stochastic Variational Bayes for non-Conjugate Inference. Proceedings of the31st International Conference on MachineLearning (ICML-14), 2014.

[25] X. Wang and D. B. Dunson. ParallelizingMCMC via Weierstrass sampler, 2013.

[26] M. Welling and Y. W. Teh. Bayesian lear-ning via stochastic gradient Langevin dyna-mics. In Proceedings of the International Con-ference on Machine Learning, 2011.

[27] J.M. Winn and C.M. Bishop. Variationalmessage passing. Journal of Machine Lear-ning Research, 2005.

[28] M. Xu, Y. W. Teh, J. Zhu, and B. Zhang.Distributed context-aware bayesian poste-rior sampling via expectation propagation.In Advances in Neural Information ProcessingSystems, 2014. s

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ISBA Bulletin, 21(4), December 2014 STUDENTS’ CORNER

STUDENTS’ CORNER

Isadora Antonianoisadora.antoniano@unibocconi.it

The Holiday season is upon us and, whateverelse that may mean for you (haters and lovers ali-ke), I hope it provides a peaceful period to catchup on your reading, for example. And if you’rerunning short on reading material, this issue ofthe Students’ Corner may be just what you need.

We open with a kind contribution from a for-mer Savage Award winner, Julien Cornebise, andhis views four years after. I won’t say more, be-cause I find his piece quite speaks for itself. Thankyou, Julien, I would have loved to read somethinglike this when I weas a Ph.D. student and I’m suresome of our readers will appreaciate it.

The second piece for this number’s Corner tellsus about blogging. The ubiquitous nature of theactivity makes for a perfect opportunity to involvedifferent people from many places and so this isa joint collaboration from an interesting group ofbloggers: Rasmus Baath (Cognitive Science, LundUniversity, Sweden), Boris Hejblum (Ph.D studentat ISPED; INSERM, Centre INSERM U897, Uni-versity of Bordeaux, and INRIA SISTM Team, F-33000 Bordeaux, France), Thiago G. Martins (Da-ta Scientist at Yahoo!), Julyan Arbel (postdoc atCollegio Carlo Alberto, Moncalieri, Italy), PierreJacob (postdoc at the University of Oxford, UK),Robin Ryder (Universite Paris-Dauphine, France),Ewan Cameron (Computational Statistician in theSpatial Ecology and Epidemiology Group, Uni-versity of Oxford, UK) and Gregory Gandenber-ger (Department of History and Philosophy ofScience, University of Pittsburgh, USA). My speci-al thanks to my dear friend Pierre for putting thetea together. Rather diverse, wouldn’t you agree?Don’t forget to check out their bloggs, you mightfind something you like.

Finally, an invitation, sent to us by FrancescaIeva, of a series of statistical events, specially de-signed for the “young” and taking place in Italy!

Don’t forget, this is your corner, suggestions,announcements, questions and contributions arealways welcome.

Happy read and happy Holidays!

On the Savage award, advices

to Ph.D. candidatesby Julien Cornebise

http://www.cornebise.com/julien/

This article is an occasion to pay forward everso slightly, by encouraging current Ph.D. candi-dates on their path, the support ISBA gave me.Four years ago, I was honored and humbled toreceive the ISBA 2010 Savage Award, categoryTheory and Methods, for my Ph.D. dissertationdefended in 2009. Looking back, I can now testifyhow much this brought to me both inside andoutside of Academia.

Inside Academia: confirming and mitigatingthe widely-shared post-graduate’s impostorsyndrome

Upon hearing of the great news, a brilliantmulti-awarded senior researcher in my lab verykindly wrote to me that such awards meant neverhaving to prove one’s worth again. Although ge-nuinely touched by her congratulations, being farless accomplished and more junior than her, I feltall the more responsible to prove myself worth ofthis show of confidence from ISBA. It would berather awkward to receive such an award only tofail miserably shortly after.

This resonated deeply with the shared secret ofrecent PhDs, discovered during my year at SAM-SI, a vibrant institution where half a dozen newpostdocs arrive each year: each and every one ofus, fresh Ph.D.s from some of the best institutions(Cambridge, Duke, Waterloo, Paris...) secretly suf-fered the very same impostor syndrome. We werelooking at each other’s CV/website and thinking“jeez! this guy/girl across the door is an expert ofhis field, look at all he/she has done, whereas Ijust barely scrape by on my own research!” –allthe while putting up a convincing facade of self-assurance in front of audiences and whiteboards,to the point of apparent cockiness. Only after can-did exchanges in SAMSI’s very open environmentdid we all discover being in the very same mind-set.

In hindsight the explanation is simple: eachyoung researcher in his/her own domain has thevery expertise to measure how much he/she still

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does not know and has yet to learn, while he/shehears other young researchers, experts in theirown other field, present results not as familiar tohim/her, thus sounding so much more advanced.This take-away from SAMSI was perfectly con-firmed by theis Savage Award: yes, maybe inde-ed, I, just like my other colleagues, might actuallyknow something relatively valuable, and my scra-ping by might just be not so bad – as is also thecase for so many of my young colleagues.

Of course, impostor syndrome is a clingy beastand, healthily, I hope to never get entirely over it –merely overcoming it enough to say “Do not wor-ry, thee young candidate, thy doubts pave a pathwell trodden”.

A similar message is also part of the little-known yet gem of a guide, How to do Researchat MIT AI Lab – Emotional Factors, relevant farbeyond its original lab. I recommend it to anyPh.D. student; the feedback from readers is una-nimous.

Outside Academia: incredibly increased reada-bility

After two post-docs, and curious to see whatwas out there in atypical paths, I took a turn outof purely academic research, first as an indepen-dent consultant, then recruited out of the blueby a start-up’s recruiter, and eventually doing mysmall share to help convince investors. I discover-ed there another facet of ISBA’s Savage Award:tremendous readability.

In Academia, the dominating metric of qualityis the length of the publication list – a debate foranother day. Outside of Academia, however, notall interlocutors know how remarkable is a JRSSBRead Paper, or an oral presentation at NIPS, or apublication in Nature.

This is where international learned societies, li-ke ISBA, come into play: the awards they bestowcan serve as headline-grabbing material in a bio-graphy, easily spotted. The interlocutors do notneed to be familiar with the subtleties of Baye-sian Analysis. All they see is a stamp of appro-val from an official association of this researcher’speers. That, in itself, is enough of a quality metricto pass the first round of contact, raise interest,and get the chance to further the conversation.

First concrete example: the recruiter who con-tacted me for the start-up I joined in 2011 wastasked to find profiles for an Applied position.The Savage Award on the CV grabbed his atten-tion, even though he had no inkling what Adap-tive Sequential Monte Carlo Methods were, nor

if they were immediately relevant to the start-up. Passing it to the start-up’s managers, they im-mediately changed focus and interviewed me fortheir Research track instead: a profile that was notwhat they were looking for originally, yet stoodout enough to interest them for a position theyhad not thought of filling via a recruiter – and in-deed a unique position that I would never havethought to find this way either!

Second concrete example, years later, hard atwork in this start-up’s amazing team: investorswere coming for a round of technical due dili-gence. Venture capitals sent their best scientists-in-residence to dive deeply into the technical de-tails of our research. Of course what matters inthe end is, and forever will be, the work that isdone and presented. Yet, the Savage Award wasmentioned in the first line of the biography thatwas sent ahead of time, as a salient point to givea strong first impression of our research team.

Advices to Ph.D. Candidates: apply, you are theworld best expert on your topic

That may sound trivial, but the first advice: ap-ply. Discuss with your advisor the possibility toput your dissertation up for consideration. Thismight sound obvious to North-American students,whose educative system is rife with awards forhigh-performing students. Not so much in France,where those would be at odds with the sometimesover-present culture of egalite in the younger-age public education system. As a cultural conse-quence, few French Ph.D. students, even the mostbrilliant, would consider putting up their disser-tation for consideration. I have been very lucky inthat regard to benefit from the advice of a long-term Bayesian, who offered to send it for me –thanks again Xi’an! Not all students, regardlesshow brilliant their work, are made aware of thispossibility.

The second advice, closely linked: do not un-derestimate the quality of your work. You are theforemost expert in the entire world on your Ph.D.topic. As discussed above, it is all too easy to seehow advanced are the maths wielded by youroffice-mate, yet oversee the as-much-advancedmaths you are juggling on a day-to-day basis,more familiar to you, and whose limitations youknow better than anyone else. Actually, knowingthese very limitations is what proves you are anexpert.

A word of thanks and final adviceFinally, a word of thanks. I have been incredibly

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lucky, throughout my career so far, to meet greatpeople. My dissertation already had four pages ofacknowledgements: I doubt the Bulletin’s editorwould appreciate me renewing (and extending!)them here. They are just as heartfelt today as theywere then. I must, of course, add ISBA and the Sa-vage Award committee for their support, as wellas all those who, by their generous donations, al-low the Savage Fund to stay alive throughout theyears.

Of interest to Ph.D. candidates, though, onespecial mention of a dual tutelage system, that Ihave seen successfully at work many times. Themost senior, a professor with the deep knowled-ge necessary to steer the project brings his end-less fonts of knowledge collected over decades,wrapped in hardened tough-love. The youngest,a postdoc or fresh assistant professor, brings vir-tuosity, emulation and day-to-day patience. In mycase they were Pr. Eric Moulines and Dr. JimmyOlsson. That might be the final advice to a stu-dent: if you ever stumble, as many do, as I mostsurely did, because Ph.D. studies can be a hell of aroller-coaster to go through, reach out to the peo-ple around you and the joint set of skills they wantto offer you. In combination, they can be amazing,and help you open doors that, in retrospect, canbe worth all the efforts.

Blogging as young researchersby Rasmus Baath from Publishable Stu↵;Boris Hejblum from Research side e↵ects;

Thiago G. Martins from tgmstat@wordpress;Julyan Arbel, Pierre Jacob and Robin Ryder

from Statisfaction; Ewan Cameron fromAnother Astrostatistics Blog; GregoryGandenberger from gandenberger.org

Inspired by established blogs, such as the po-pular Statistical Modeling, Causal Inference, andSocial Science or Xi’an’s Og, each of us beganblogging as a way to diarize our learning adven-tures, to share bits of R code or LaTeX tips, andto advertise our own papers and projects. Alongthe way we’ve come to a new appreciation of theworld of academic blogging: a never-ending in-ternational seminar, attended by renowned scien-tists and anonymous users alike. Here we shareour experiences by weighing the pros and cons ofblogging from the point of view of young resear-chers.

[To blog...]

At least at face value blogging has some notableadvantages over traditional academic communi-cation: publication is instantaneous and thus pro-ves efficient in sparking discussions and debates;it allows all sorts of technological sorcery (hyper-links, animations, applications), while many jour-nals are still adapting to grayscale plots; and itallows for humorous and colourful writing styles,freeing the writer from the constraints of the im-personal academic prose. Last but not least, it isacceptable to blog about almost any topic, fromoffice politics to funding bodies, from complaintsabout the absurdity of p-values to debates on thenet profits of publishing companies, not to menti-on quarrels about the term “data science”.

For young researchers, some aspects are par-ticularly appealing. By putting academics direct-ly in touch with one another through commentsand replies, young researchers are given the op-portunity to “talk” directly on technical subjectsto some of the most renowned experts in theirfields–and indeed a surprising number of seni-or researchers are avid blog readers! This oftenproves much more efficient than trying to awk-wardly stalk the same professors at conferences.Through such interactions, young academics canshow off their many interests and skills, which cando much to fill out the picture painted by theiracademic CV.

Beyond those low and careerist considerations,we see blogging as a good tool to learn and toshare scientific ideas. According to popular belief,only a third of all started research projects end upin a publication; but all of them can at least endup on a blog. So if you indulge in a bit of off-topicstudy or burn a few hours playing around witha new methodology it need not fuel your perfor-mance anxiety: a blog post explaining it will stillfeel like a delivered product. And you will very li-kely get some interesting feedback–though rarelyto the depth given in journal reviews.

Finally, using blogs to advertise articles andpackages seems particularly useful at the earlystage of a career, where you might not be invitedto that many conferences, or might only be givensome dark corner of a giant poster session to talkabout your work.

[... or not to blog ?]

Some cautionary notes now, blogging can berisky! As the adage goes, “better to keep yourmouth shut and appear a fool than to open it andremove all doubt”. Beyond the quality of the con-tent being shared, blogs are also sometimes disre-

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garded by academics as a frivolous medium; thereis a risk that your colleagues will see your blog-ging hobby as a pure waste of time.

A second risk is to disclose too much informati-on about promising research leads. There shouldbe some balance between ideas shared and ideaskept secret, so that blogging does not jeopardizepublication. Other platforms that formally esta-blish precedence (such as arXiv) might be bettersuited for the initial presentation of new and exci-ting work. For this reason it seems wisest to blog aposteriori, though the interest of these blogs willbe less than their potential to function as real timeresearch diaries.

A third risk is genuine time-wasting. For thosewho have never tried, it can be surprising to dis-cover how many hours are needed to write eachpost. It can be frustrating in the beginning whenreader statistics indicate an audience of just oneor two spam-bots and some curious relatives. Onthe other hand there are still a limited number ofacademic blogs on statistics so far, so the marketis far from saturation: any new blog can quicklygarner a decent amount of attention. Of course itcan be hard to keep a regular posting schedule,which is necessary to maintain a stable readingbase.

To conclude, blogging can be a clever way tobypass the hierarchical structure of academia. Itgives everyone a direct and fast access to everyo-ne else. In some respects it helps to alleviate keyproblems affecting young researchers, such as thelengthy reviewing process of top journals and thelack of communication space.

Invitation:StaTalk on Biostatistics

Department of Mathematics “F. Enrique”,

Universita degli Studi di Milano, via Saldini50, Milano (Italy)

We would like to announce the first StaTalk pro-moted by the young section of Italian StatisticalSociety (y-SIS), to be held in Milan, on April 24th,2015.

All the information, including the tentative pro-gram and the sign-up link for the conference, canbe found on the conference website.

StaTalks are a series of one-day workshops ai-med at early career statisticians, i.e., master de-gree students, people carrying out a PhD, post-docs and young researchers. StaTalks are a fan-tastic opportunity to share knowledge on advan-ced topics of statistics in a relaxed and friend-ly atmosphere. For each talk, a hosting researchgroup will propose tutorials, seminars and inter-active round tables related with its main fields ofresearch and application. Participants can contri-bute bringing their own experience through pre-sentations, ideas, posters and material to be sha-red during the debate. A StaTalk will be held eve-ry 6 months in a different city in Italy. StaTalks onBayesian Nonparametrics and on ComputationalTools for Statistical Modelling are already plan-ned in Turin (autumn 2015) and Rome (spring2016), respectively.

Registrations to the first StaTalk are now open.No conference fees are due, but registration ismandatory, using the form available on the web-site.

For further information, doubts or questions,please visit the event website and do not hesitateto contact the y-SIS officers: Francesca Ieva, Sere-na Arima, Antonio Canale, Clara Grazian and Ma-riangela Sciandra, at the address e-mail youngs-is.2013@gmail.com.

NEWS FROM THE WORLD

Announcements

BNP10: BNP10 contributed talks and postersdeadline extended

The 10th conference on Bayesian Nonpara-metrics (BNP10) will be held in June 22-26,2015 in Raleigh, NC. BNP10 will feature four

special invited lectures, eight invited sessions,contributed talks and poster sessions. A pro-posal for a contributed talk or poster can bemade through the conference website http:

//www.stat.duke.edu/bnp10/ and should con-sist of a title and an abstract. Slots for contributedtalks are limited.

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ISBA Bulletin, 21(4), December 2014 NEWS FROM THE WORLD

Deadline for submission of a contributed talkhas been extended to January 15, 2015, and de-cisions about acceptance for contributed talk isexpected by February 15, 2015. Deadline for po-ster submission is May 1, 2015.

If you have any questions, please contactthe BNP10 Local Organizing Committee at10thbnpmeeting@gmail.com.

Member News

Mike West is recipient of award from ASA NCChapter

Sunday, October 12, 2014 - 4:30pm

Mike West, the Duke University distinguishedArts & Sciences Professor of Statistics and Decisi-on Sciences, has been honored with a distinguis-hed award from the NC Chapter of the AmericanStatistical Association. The award was announcedand presented at the October 10-12, 2014 Inter-

national Conference on Advances in Interdiscipli-nary Statistics and Combinatorics. West, a plenaryspeaker at the conference, was recognized for hisoutstanding contributions to the theory and prac-tice of statistics, and particularly for his role in thedevelopment of the subject in North Carolina.

Jerry Reiter, current president of the NC Chapterof the American Statistical Association, announ-ced and presented the award to Mike West at theconference award ceremony.

s

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ISBA Bulletin, 21(4), December 2014

Executive Committee

President: Sonia PetronePast President: Merlise ClydePresident Elect: Alexandra M. SchmidtTreasurer: Mural HaranExecutive Secretary: Steve Scott

Program Council

Chair: Ramses MenaVice Chair: Michele GuindaniPast Chair: Raquel Prado

Board Members:

2013–2015:Paul Fearnhead, Amy Herring, Jaeyong Lee,Havard Rue

2012–2014:Kate Calder, Maria de Iorio, Igor Prunster,Wess Johnson

2011–2013:Ming-Hui Chen, Chris Holmes, AntoniettaMira, Judith Rousseau

EDITORIAL BOARD

Editor

Feng Liangliangf@illinois.edu

Associate Editors

Students’ CornerIsadora Antoniano & Antonio Ortizisadora.antoniano@unibocconi.it

aao33@kentforlife.net

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