CS433 : Modeling and Simulation

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CS433 : Modeling and Simulation. Lecture 10: Discrete Time Markov Chains Dr. Shafique Ahmad Chaudhry Department of Computer Science E-mail: Office # GR-02-C Tel # 2581328. Markov Processes. Stochastic Process X ( t ) is a random variable that varies with time.
CS433 : Modeling and Simulation

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Slide 1

CS433 : Modeling and Simulation

Lecture 10:

Discrete Time Markov Chains

Dr. Shafique Ahmad Chaudhry

Department of Computer Science


Office # GR-02-C

Tel # 2581328

Slide 2

Markov Processes

  • Stochastic Process X(t)is a random variable that varies with time.

  • A state of the process is a possible value of X(t)

  • Markov Process

    • The future of a process does not depend on its past, only on its present

    • a Markov process is a stochastic (random) process in which the probability distribution of the current value is conditionally independent of the series of past value, a characteristic called the Markov property.

    • Markov property: the conditional probability distribution of future states of the process, given the present state and all past states, depends only upon the present state and not on any past states

  • Marko Chain: is a discrete-time stochastic process with the Markov property

Slide 3

Classification of States: 1

Apathis a sequence of states, where each transition has a positive probability of occurring.

State jis reachable (or accessible)(يمكن الوصول إليه) from state i(ij) if there is a path from i to j –equivalently Pij(n)> 0 for some n≥0, i.e.the probability to go from ito j in nsteps is greater than zero.

States i and j communicate (ij)(يتصل) ifiis reachable fromjandjis reachable fromi.

(Note: a state i always communicates with itself)

A set of states C is a communicating classif every pair of states in C communicates with each other, and no state in C communicates with any state not in C.

Slide 4

Classification of States: 1

A state i is said to be an absorbing state if pii= 1.

A subset S of the state space Xis a closed set if no state outside of S is reachable from any state in S (like an absorbing state, but with multiple states), this means pij= 0for every iS and j S

A closed set S of states is irreducible(غير قابل للتخفيض) if any state j Sis reachable from every state iS.

A Markov chain is said to be irreducible if the state space X is irreducible.

Slide 5


Irreducible Markov Chain
























  • Reducible Markov Chain

Absorbing State

Closed irreducible set

Slide 6

Classification of States: 2

State iis atransient state(حالة عابرة)if there exists a state j such that j is reachable from ibut i is not reachable from j.

A state that is not transient is recurrent(حالة متكررة) . There are two types of recurrent states:

Positive recurrent: if the expected time to return to the state is finite.

Null recurrent (less common): if the expected time to return to the state is infinite(this requires an infinite number of states).

A state iis periodic with periodk>1, ifkis the smallest number such that all paths leading from state iback to state i have a multiple of k transitions.

A state is aperiodic if it has period k =1.

A state is ergodic if it is positive recurrent and aperiodic.

Slide 7

Classification of States: 2

Example from Book

Introduction to Probability: Lecture Notes

D. Bertsekas and J. Tistsiklis – Fall 2000

Slide 8

Transient and Recurrent States

We define the hittingtime Tijas the random variable that represents the time to go from state j to stat i, and is expressed as:

k is the number of transition in a path from i to j.

Tijis the minimum number of transitions in a path from i to j.

We define the recurrence timeTii as the first time that the Markov Chain returns to state i.

The probability that the first recurrence to state ioccurs at the nth-step is

TiTime for first visit to i given X0 = i.

The probability of recurrence to state iis

Slide 9

Transient and Recurrent States

  • The mean recurrence time is

  • A state is recurrent if fi=1

    • If Mi < then it is said Positive Recurrent

    • If Mi =  then it is said Null Recurrent

  • A state is transient if fi<1

  • If , then is the probability of never returning to state i.

Slide 10

Transient and Recurrent States

We define Niasthe number of visits to stateigiven X0=i,

Theorem: If Ni is the number of visits to state igiven X0=i,then


Transition Probability from

state i to state i after n steps

Slide 11

Transient and Recurrent States

The probability of reaching state j for first time in n-steps starting from X0 = i.

The probability of ever reaching j starting from state i is

Slide 12

Three Theorems

If a Markov Chain has finite state space, then: at least one of the states is recurrent.

If state i is recurrent and state j is reachable from state i

then: state j is also recurrent.

IfS is a finite closed irreducible set of states, then: every state in S is recurrent.

Slide 13

Positive and Null Recurrent States

Let Mi be the mean recurrence time of state i

A state is said to be positive recurrent if Mi<∞.

If Mi=∞ then the state is said to be null-recurrent.

Three Theorems

If state i is positive recurrent and statej is reachable from state i then, state j is also positive recurrent.

If S is a closed irreducible set of states, then every state in S is positive recurrent or, every state in S is null recurrent, or, every state in S is transient.

If S is a finite closed irreducible set of states, then every state in S is positive recurrent.

Slide 14









Positive Recurrent States

Transient States








Recurrent State

Slide 15

Periodic and Aperiodic States

Suppose that the structure of the Markov Chain is such that state i is visited after a number of steps that is an integer multiple of an integer d >1. Then the state is called periodic with period d.

If no such integer exists (i.e., d =1) then the state is called aperiodic.









Periodic State d = 2

Slide 16

Steady State Analysis

Recall that the state probability, which is the probability of finding the MC at state i after the kth step is given by:

  • An interesting question is what happens in the “long run”, i.e.,

  • This is referred to as steady stateor equilibrium or stationary state probability

  • Questions:

    • Do these limits exists?

    • If they exist, do they converge to a legitimate probability distribution, i.e.,

    • How do we evaluate πj, for all j.

Slide 17

Multi-step (t-step) Transitions

Example: TAX auditing problem:

Assume that whether a tax payer is audited by Tax department or not in the n + 1 is dependent only on whether he was audit in the previous year or not.

  • If he is not audited in year n, he will not be audited with prob 0.6, and will be audited with prob 0.4

  • If he is audited in year n, he will be audited with prob 0.5, and will not be audited with prob 0.5

How to model this problem as a stochastic process ?


Slide 18

The Tax Auditing Example

State Space: Two states: s0 = 0 (no audit), s1 = 1 (audit)

Transition matrix

Transition Matrix P is the prob. of transition in one step

How do we calculate the probabilities for transitions involving more than one step?

Notice: p01 = 0.4, is conditional probability of audit next year given no audit this year.

p01 = p (x1 = 1 | x0 = 0)



Slide 19

n-Step Transition Probabilities

This idea generalizes to an arbitrary number of steps:

In matrix form, P(2) = P  P,

P(3) = P(2) P = P2 P = P3

or more generally

P(n) = P(m) P(n-m)

The ij'th entry of this reduces to

Pij(n) = Pik(m) Pkj(n-m) 1  m  n1



Chapman - Kolmogorov Equations

“The probability of going from i to k in m steps & then going from k to j in the remaining nm steps, summed over all possible intermediate states k”

Slide 20

Steady-State Solutions – n Steps

What happens with t get large?


Slide 21

Steady-State Solutions – n Steps


Slide 22

Steady State Transition Probability

Observations: as n gets large, the values in row of the matrix becomes identical OR they asymptotically approach a steady state value

What does it mean?

The probability of being in any future state becomes independent of the initial state as time process

 j = limn Pr (Xn=j |X0=i } = limnpij (n) for all i and j

These asymptoticalvalues are calledSteady-State Probabilities


Slide 23

Steady State Analysis

Recall the recursive probability

  • If steady state exists, then π(k+1)π(k), and therefore the steady state probabilities are given by the solution to the equations


  • If an Irreducible Markov Chain, then the presence of periodic states prevents the existence of a steady state probability

Slide 24

Steady State Analysis

  • THEOREM: In an irreducible aperiodic Markov chain consisting of positive recurrent states a unique stationary state probabilityvector π exists such that πj > 0 and

where Mj is the mean recurrence time of state j

  • The steady state vector πis determined by solving


  • Ergodic Markov chain.

Slide 25

Comments on Steady-State Results

1. Steady-state probabilities might not exist unless the Markov chain is ergodic.

2.Steady-state predictions are never achieved in actuality due to a combination of

(i) errors in estimating P,

(ii) changes in P over time, and

(iii) changes in the nature of dependence relationships among the states.

Nevertheless, the use of steady-state values is an important diagnostic tool for the decision maker.


Slide 26

Interpretation of Steady-State Conditions

Just because an ergodic system has steady-state probabilities does not mean that the system “settles down” into any one state.

j is simply the likelihood of finding the system in state j after a large number of steps.

The limiting probability πj that the process is in state j after a large number of steps is also equals the long-run proportion of time that the process will be in state j.

When the Markov chain is finite, irreducible and periodic, we still have the result that the πj, j Î S, uniquely solves the steady-state equations, but now πj must be interpreted as the long-run proportion of time that the chain is in state j.


Slide 27

Discrete Birth-Death Example











  • Thus, to find the steady state vector πwe need to solve


Slide 28

Discrete Birth-Death Example

  • In other words

  • Solving these equations we get

  • In general

  • Summing all terms we get

Slide 29

Discrete Birth-Death Example

  • Therefore, for all states j we get

  • If p<1/2, then

All states are transient

  • If p>1/2, then

All states are positive recurrent

Slide 30

Discrete Birth-Death Example

  • If p=1/2, then

All states are null recurrent

Slide 31

Reducible Markov Chains

Transient Set T

Irreducible Set S1

Irreducible Set S2

  • In steady state, we know that the Markov chain will eventually end in an irreducible set and the previous analysis still holds, or an absorbing state.

  • The only question that arises, in case there are two or more irreducible sets, is the probability it will end in each set

Slide 32

Reducible Markov Chains

Transient Set T

Irreducible Set S





  • Suppose we start from state i. Then, there are two ways to go to S.

    • In one step or

    • Go to r T after k steps, and then to S.

  • Define

Slide 33

Reducible Markov Chains

  • First consider the one-step transition

  • Next consider the general case for k=2,3,…

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