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Spatial Birth-Death-Swap ChainsWhy swapping at birth is a good thing
Mark Huber
Department of MathematicsClaremont-McKenna College
14 Jan, 2010
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Competition is everywhere
Towns compete for space Trees compete for sunlight
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Effects of competition
For spatial data...points look like they are repelling one anothermore regularly spaced than if locations independent
ModelsBegin with standard Poisson point processPenalize configurations where points are close together
TodayNeed to be able to sample from models to analyzebehaviorA new Markov chain method for dealing with these models
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Effects of competition
Markov chainsmake small random changes to configurationbirth=add a pointdeath=remove a point
Todayswap=one point added + one point removedFirst used for discrete time and space (Luby, Vigoda 1997)Here extended to continuous time and spaceResult: faster chain (theoretically and experimentally)
Mark Huber, Claremont-McKenna College Spatial Birth-Death-Swap Chains., Why swapping at birth is a good thing 4/23
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Continuous time Birth Death chains
Jump processes:Points born at times separated by time Exp(λ · area(S)
When point born, decide “lifetime" that is Exp(1)
After lifetime, the point dies and is removed from processStationary distribution Poisson point process
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Illustration of birth death chain
time
birth 1 birth 2 birth 3death 1
Exp(1)
death 3
Exp(1)
Exp(1)
1 1
2 2 23 2
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Purpose of birth death chains
Metropolis-HastingsBirth Death process plays role of proposal chainPreston’s [3] approach: always accept deathsOnly sometimes accept briths
By only accepting some births...Ensures jump process equivalent of reversibilityWorks for locally stable densities
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Hard core model: example of rejected birthHard core means discs are not allowed to overlapNew point (in blue) is rejected as too close to existing points
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Example of accepted birthNew point (in blue) is accepted an added to configurationToo close to existing points
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Example of deathDeaths are always accepted(Removing point never violates hard core constraint)
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To speed up chain, add a move
Old movesBirth: addition of pointDeath: removal of point
New moveSwap: addition and removal happen simulataneously
HistoryUsed in discrete context by Broder (1986) for perfectmatchingsUsed for discrete hard core processes by Luby & Vigoda(1997)
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Example of swap for hard core gas modelWhen blocked by exactly one point, “swap” with blocking point:
⇒
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Some details
Things to consider:Does swapping give correct distribution?Does it improve performance in a theoretical way?Does the swap move generalize?
Preprint: Huber [2]Can set up probability of swapping to give correctdistributionRates for swap either direction must be equalDoes work faster than original chainSpeeds up perfect simulation algorithm
Mark Huber, Claremont-McKenna College Spatial Birth-Death-Swap Chains., Why swapping at birth is a good thing 13/23
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Some details
Things to consider:Does swapping give correct distribution?Does it improve performance in a theoretical way?Does the swap move generalize?
Preprint: Huber [2]Can set up probability of swapping to give correctdistributionRates for swap either direction must be equalDoes work faster than original chainSpeeds up perfect simulation algorithm
Mark Huber, Claremont-McKenna College Spatial Birth-Death-Swap Chains., Why swapping at birth is a good thing 13/23
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Perfect Sampling
“Practice makes perfect, but nobody’s perfect, so why practice?”
Problem with Markov chainsHow long should they be run?Perfect sampling algorithms share good properties ofMarkov chains......but terminate in finite time (with probability 1)
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Dominated Coupling From the PastKendall and Møller [1]: Keep track of unknown point overlifetime:
Say we don’t know if a point should be in the set or notIf it dies, great!If point born within range before it dies, bad
time→
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When is procedure fast?
The unknown points (?) are like an infectionDie out when average # of children < 1Let a be area of ball of radius RAverage # children before death is λa
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When are you guaranteed good performance?
Theorem (Huber [2])Running time of dCFTP (no swap) is Θ(µ(S) lnµ(S)) forλ ≤ f (R)
Theorem (Huber [2])Running time of dCFTP (sometimes swap) is Θ(µ(S) lnµ(S))for λ ≤ 2f (R)
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Swap moves can help or hurt...
Situation 1: When only a single ? in range, swapping helps:
Situation 2: When more than one neighbor in range, swappinghurts:
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Sometimes swapping
When given opportunity to swap:Execute swap with probability pswap
Otherwise no swapSet pswap = 1/4:
Situation 1: +1 ?’s with prob 3/4, -1 ?’s with proba 1/4Situation 2: 0 ?’s with prob 3/4, +2 ?’s with prob 1/4Either way: rate of ?’s is(+1)(3/4) + (−1)(1/4) = 2(1/4) = 1/2
Effectively, ?’s born at half the rate they were with no swapmove
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Running time results
20 30 40 50 60 700
1000
2000
3000
4000
5000
λ
Ave
rage
num
ber
of e
vent
s pe
r sa
mpl
e
Running time of dCFTP for hard core gas model
no swapswap
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Conclusions about swap move
What is known:Swap move easy to add to point processesAlso can be used in dCFTP to get perfect samplingalgorithmResults in about a 4-fold speedup for hard-core gas model
Future work:Running time comparison for Strauss processImprovement near phase transitionExperiment better than theory–can theory be improved?
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References
W.S. Kendall and J. Møller.Perfect simulation using dominating processes on ordered spaces, withapplication to locally stable point processes.Adv. Appl. Prob., 32:844–865, 2000.
M. L. Huber.Spatial Birth-Death-Swap Chainspreprint, 2007
C.J. Preston.Spatial birth-and-death processes.Bull. Inst. Int. Stat., 46(2):371–391, 1977.
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