1 introduction to stochastic processes and markov chain prof. dr. md. asaduzzaman shah department of...
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Introduction to Stochastic Processes and Markov Chain
Prof. Dr. Md. Asaduzzaman Shah Department of Statistics
University of Rajshahi
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Variable
Values varies from individual to individual
Stochastic Variable
Families of random variable
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One dimensional process
Classified into four types of processes
(i) Discrete time, discrete state space (ii) Discrete time, continuous state space (iii) Continuous time, discrete state space (iv) Continuous time, continuous state space
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Discrete time Discrete state space
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Examples
A fair coin is tossed n times and the outcomes represented by Xn. Here, Toss No. treated as ‘time’ and the result/outcome considered as ‘space’ of the experiment. Both time and space are discrete.
Toss No. 1 2 3 … n-1 n
Outcomes T H H … H T
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Experiment and Its Outcomes
Suppose a fair coin is tossed twice. Sample space and four simple events are
4321 ,,,,,, TTTHHTHH
TTandTHHTHH 4321 ,,
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Counting Heads
0)()(
1)()(
1)()(
2)()(
4
3
2
1
TTXX
THXX
HTXX
HHXX
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Probability
Now the values 0,1 and 2 have probabilities ¼, 2/4=1/2 and ¼. So, X(t) = X(ω) = 0,1,2 are stochastic variable with t=0,1,2.
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Discrete time Continuous state space (Velocity of a car in a time interval (0,t)
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DC
In a stochastic process {X(t), t€T} in which time parameter, t is discrete, however, the state space, velocity X(t), which is increasing or decreasing is continuous.
Time(Road) 10:00:00 12:30:00 18:45:00 24:00:00
Velocity 60m/h 45m/h 26m/h 70m/h
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Continuous time, discrete state space
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CD
The number of passengers waiting at Dhaka Bus Terminal, Rajshahi on Friday, August 09, 2011 from (10-11 AM).Time and tide wait for none(Continuous).
Time 10:00 10:15 10:20 11:00
Passengers
waiting460 345 220 370
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More examples
Long Route Buses: Hanif, National , Green Line, etc. that’s are started in a particular time.
Patients are admitted at Rajshahi Medical College Hospital (RMCH) in a particular day.
Customer’s are waiting in a super-market for getting Eid Fashion
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Continuous time Continuous state space
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CC
If we measure the temperature of a certain place in a time interval (0,t) for each day is an example of CTCSS, because time interval is continuous as well as temperature which is not fixed, it is rising/falling following a day follows morning to noon, noon to evening, evening to night, so state space is also continuous.
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Loss variables character
** Making Laugh to see the teeth (^^^^^^) instead of monitoring the defective teeth
** Slapping to see the number of eye drops (weeping),
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Notations
Discrete random variable represented by Xn or X(n).
Continuous random variable indicated by Xt or X(t).
Stochastic processes are defined by the notations { Xn} and {X(t)}
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Differences
Discrete Continuous
Family represented
by {Xn, n=0,1,2,…,} Family represented by {Xt, t€T} or
{X(t), t€T}, T is finite/infinite.
Parameter ‘n’ Parameter ‘t’ interpreted as time, some times it represents characters like as, (i) distance, (ii) length, (iii) thickness and so on.
Stochastic sequence Stochastic process
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At a Glance
Xn
X(t)
Xn
Time
X(t)
Thrown/Toss No.
Distance/Length/
Thickness.
Outcomes of ExperimentOr Space
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Relationship
In some of the cases, the r.v Xn, i.e. members of the family {Xn, n>=0} are mutually independent, but more often they are not independent. We generally come across Processes whose members are mutually dependent.
Throwing a fair coin 20 times by 20 peoples, outcomes will be different instead of, a person will throw the coin 20 times. The relationship among them is often of great importance.
The nature of dependence could be infinitely varied. Dependence of such special types, which occurs quite often and is
of great importance. According to the nature of dependence relationship existing among
the members of the family, we may describe or mathematically formulate them by some Stochastic Processes.
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Stationary Processesstochastic process { Xn, n>=1 } is time independent (not necessary hours, months, years) i.e. covariance stationary.
Let Xn, n>=1 be uncorrelated random variables with mean 0 and variance 1.
nmifnmif
mn
mnmnmn
XXE
XEXEXXEXXmnC
01
),(
)(.)(),(,cov,
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1. Poisson Process
Consider the process {X(t), t€T} with
;...;3,2,1,0,0,!
)()(Pr
nn
tentX
nt
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(i) the mean function and (ii) the variance
ttXEtm )()(
ttX )(var
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Non-Stationary
Both are functions of t. Therefore, the stochastic process {Xn, n>=1} is non-stationary/evolutionary. The distribution (Poisson) of the stochastic process {Xn, n>=1} is functionally dependent on t.
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2. Mean and Co-variance
tddtDDEtXEtm 2121)()(
222
212
11
22
2221
21
21
2221
21
2121
))(()()(
)())(()(
)()(
)()()()(
))(()()(
DEDVartimesofproduct
ddtimesofsumDEDVar
dstddtsd
DEstDDEtsDE
sDDtDDEsXtXE
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Variance
)(2
))(()(var)(
))(()()(var
22
22
221
21
21
221
22
221
22
22
dtdtddtddt
tXEtXtXE
tXEtXEtX
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The Process is non-stationary, i.e. evolutionary
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21)(),(cov),( tssXtXtsC
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3. Expection
2210)( tDtDDtX
0.00.0)()()()]([ 22
210 ttDEtDEtDEtXE
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Covariance
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2210
2210
2210
2210
1
)()()})({(
))(()).(()}()({)(),(
stts
sDsDDEtDtDDEsDsDDtDtDDE
sXEtXEsXtXEsXtXCov
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Variance
422
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20
2210 1)()()()var{)}({ ttDvtDvtDvtDtDDtXVar
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Mean stationary but Process non-stationary
Therefore, the stochastic process, X(t)=D0 + D1 t + D2 t2, where Di, i=0,1,2 are
uncorrelated random variables with mean 0 and variance 1, is dependent on the time parameter t and s, so it is non-stationary. But the mean of the process is stationary.
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Briefly: Time, also treated as distance, length, thickness etc.
StationaryTime- Independent
Non-Stationary
Time- Dependent
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Problem
Manufacturers A and B are competing with each other in a restricted market. Over the year, A’s customers have exhibited a high degree of loyalty as measured by the fact that customers using A’s product 80 percent of the time. Also former customers purchasing the product from B have switched back to A’s 60 percent of the time.
(a) Construct and interpret the state transition matrix terms of (i) retention and loss, (ii) retention and gain.
(b) Calculate the probability of a customer purchasing A’s product at the end of the second period, Draw the transition probability diagrams and the transition trees.
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State Transition P
Current Purchase
(n=0)
Next Purchase (n=1)
A B
A 0.8 0.2
B 0.6 0.4
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Interpretation of Conditional Probability
The probability of a customer’s purchase at the next step (n=1) depends upon the product that he bought previously (n=0), i.e., current purchase.
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Retention and Loss (Rows of P),
P11= 0.80
means that a customer now using A’s product will again purchase A’s product at the next purchase in 8 of 10 times. This implies retention to A’s product.
P12= 0.20
means that the customer now using A’s product will switch over to B’s product at the next purchase in 2 pot of 10 times. This implies loss of A’s product.
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Retention and Gain (Columns of P),
P11= 0.80
means that the customer now using A’s product will again purchase A’s product next time in 8 out of 10 times. This implies retention of A’s product.
P12= 0.60
shows that the customer now using B’s product will purchase A’s product next time in 6 out of 10 times. This implies gain to A’s product.
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Second Row and Column
A similar interpretation holds for the second row of P
The second column of P can be interpreted similarly.
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Transition Probability Diagram
0.80 A B 0.40
0. 200.
60
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Transition Trees
A
A
B
A
A
B
B
0.80
0.20
0.80
0.20
0.40
0.60
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QUESTIONNAIRE ON PROFESSIONS (Research Project & Field Studies)
•AgricultureBusinessServiceOthers
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Generations and their professions
(Please put tick () mark on the Boxes)
Agriculture
Service
Others
Business
Great Grand FatherGrand Father
Father
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Raw data table
ProfessionsGreat Grand Father Grand Father Father
A B S O A B S O A B S O
X X X X X X X X X
X X X X X X X X X
X X X X X X X X X X X X X X X X X X
X X X X X X X X X
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NUMERICAL FIGURES IN DIFFERENT STATES OF CLASSES
20062421149Total
165434O
60501S
1810116B
1600157138A
OSBA
TotalG G Father
G Father
Pro
fess
ions
Professions
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ESTIMATED TRANSITION PROBABILITY
A B S O
A 0.8625 0.0437 0.0937 0.0000
B 0.3333 0.6111 0.0000 0.0555
S 0.1666 0.0000 0.8333 0.0000
O 0.2500 0.1875 0.2500 0.3125
G G Father
G Father
Pro
fess
ions
Professions
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EXPECTED STAY IN EACH SOCIAL CLASS AND PRAISE MEASURE OF SOCIAL MOBILITY
1.4904 1.0064 1.4999 Others
1.82552.73905.0000 Service
2.2702 1.21152.7503 Business
2.2854 1.2253 2.8003 Agriculture
Class )1(
1)(
ijj p
E
)1(
1)(
jj p
E
)(
)(
j
jj E
E
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COMMENT
Last column of the above Table states that the generation of Great Grand Father of Grand Father, the agriculture community has most tendency to adjust their children to agriculture. Then comes the Business community and third after Agriculture and Business community comes the Service community. Finally comes the others community.
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Bartholomew Model
k
j
k
iiji jipf
1 1
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This measure depends on which generation we choose as our base. Replace fi by pj we get
measure of social mobility given by
10,1 1
DjippDk
j
k
iijj
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Conditions
D = 0,
the society is perfectly stable i.e, no mobility takes place
D = 1,
the society is perfectly mobile.
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Application Bartholomew Method
D = p1 [ p12+ 2p13 + 3p14 ] + p2 [ p21+ p23
+ 2p24 ] +
p3 [ 2p31+ p32 + p34 ] + p4 [ 3p41+ 2p42
+ p43 ]
= 0.2242
From the Bartholomew co-efficient of mobility we see that the society of that time was quite stable one.Indicates that the society in the generation of Great Father to Grand Father had a very good deg of mobility which is an indicator of the economic and social progress the society is going through in this generation.
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Chi-Square Test
Simplest and most commonly used non-parametric test in statistical work. The quantity describes the magnitude of discrepancy theory and observation.
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Hypothesis
The transition probability matrix (t.p.m) in internal is completely independent of the lithology at the immediate underlying point. Under this hypothesis the expected t.p.m would consist of rows that are all identical to the fixed probability vector.
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Theory
ectedEobservedO
E
EO
j j
jj
exp,
,)(4
1
22
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Values
)(48.9
)(39.142
05.0,4
2
talculated
calculated
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Decision
So the null hypothesis is rejected as the tabulated value is lower than the calculated value.
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NUMERICAL FIGURES IN DIFFERENT STATES OF CLASSES
TotalA B S O
A 75 31 40 3 149
B 0 15 6 0 21
S 5 3 15 1 24
O 3 0 1 2 6
Total 149 21 24 6 200
Professions
Pro
fess
ions
Father
G Father
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EXPECTED STAY IN EACH SOCIAL CLASS AND PRAISE MEASURE OF SOCIAL MOBILITY
Class
Agriculture 1.8923
Business 1.2845
Service 2.3233
Others 1.3529
)(
)(
j
jj E
E
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COMMENT
From the above Table, the generation from Grand Father to Father, the service community has most tendency to adjust their son to service. Second comes to agriculture community and third comes the business.
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D-index & Chi-square
Using the measure of mobility as given by Bartholomew, D = 0.6031, A good degree of mobility, indicates fast social and economic progress.
Null hypothesis accepted, generations have independence in profession selections
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Occupational Mobility : Markov Approach
Human Societies Stratified
Income Occupation Social Status
Residence Place
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societies move on class
• In our society children do not always follow their fathers’ footsteps.
• In a free society a person has some degree of choice about changing his job or moving house.
• The inherent uncertainty of individual behavior in these situations means that the future development of the mobility process cannot be predicted with certainty but only in terms of probability.
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MODELS FOR SOCIAL MOBILITY
• A very simple model for the development of a single family line and then, investigate the consequences of unrealistic features. The fundamental requirement in a model is that it must specify the way in which changes in social occur.
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Assumption of model• Chance of moving depends only on the
• present class • but not on the
• past class/remote past• If the movement can be regarded as taking place at
discrete points in time the appropriate model becomes a simple
• Markov chain• Changes are governed by transition probabilities
which are independent of time. 65
Markov
• Andrei Andreivich Markov
• Alive 67 years
• From1856 to 1922
• Russian Mathematician
• Idea first occurred to him when he was watching an opera of the famous Russian writer Pushkin’s.
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Notations
• Pij, the probability that the son of a father in class i is in class j (since the system is closed)
• 1
1
k
jijP
k is the number of classes and P, the matrix of transition probabilities.
TPpTP )0()(
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Day-to-Day Inventory
Problem : The number of units of an item that are withdrawn from inventory on a day-to-day basis is a Markov chain in which requirements for tomorrow depend on today’s requirements. A one day transition matrix is given below :
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Number Units Withdrawn from Inventory
6.03.01.012
4.03.03.010
0.04.06.05
12105
Today
Tomorrow
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Target
• (a) Tree diagram showing inventory requirements on two consecutive days.
• (b) A two-day transition matrix
• (c) How a two-day transition matrix help manager for inventory management.
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Transition Tree
5 10
5
12
5
10
12
5
10
12
10
12
5
0.6
0.4
0.0
0.6
0.3
0.1
0.4
0.3
0.3
0.0
0.4
0.6
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Transition Probabilities
48.0)1.0)(0.0()3.0)(4.0()6.0)(6.0(211 p
36.0)3.0)(0.0()3.0)(4.0()4.0)(6.0(212 p
16.0)6.0)(0.0()4.0)(4.0()0.0)(6.0(213 p
48.0)1.0)(0.0()3.0)(4.0()6.0)(6.0(211 p
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Transition Probabilities
31.0)1.0)(4.0()3.0)(3.0()6.0)(3.0(221 p
33.0)3.0)(4.0()3.0)(3.0()4.0)(3.0(222 p
36.0)6.0)(4.0()4.0)(3.0()0.0)(3.0(223 p
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Transition Probabilities
21.0)1.0)(6.0()3.0)(3.0()6.0)(1.0(231 p
31.0)3.0)(6.0()3.0)(3.0()4.0)(1.0(232 p
48.0)6.0)(6.0()4.0)(3.0()0.0)(3.0(233 p
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