Markov Chains (2) - Sharifce.sharif.edu/courses/93-94/1/ce634-1/resources/root... · 2020. 9....

Markov Chains (2)

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Outlines

Discrete Time Markov Chain (DTMC) …

Continuous Time Markov Chain (CTMC)

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Discrete Time Markov Chain (DTMC)…

denotes the pmf of the random variable

We will only be concerned with homogenous Markov

chains. For such chains, we use the following notation to

denote n-step transition probabilities.

The one-step transition probabilities are simply

written as , thus:

( ) ( )j np n P X j

( )jp n

( ) ( | )jk m n mp n P X k X j

1(1) ( | )jk jk n np p P X k X j

(1)jkp

jkp

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The pmf of the random variable , often called the initial

probability vector, is specified as

The one-step transition probabilities are compactly specified

in the form of a transition probability matrix

The entries of the matrix P satisfy the following two properties

0 1p(0) [ (0), (0),...]p p

0X

00 01 02

10 11 12

.

.P [ ]

. . . .

. . . .

ij

p p p

p p pp

, ;i j I and .i I0 1,ijp 1,ij

j I

p

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An equivalent description of the one-step transition

probabilities can be given by a directed graph called the

state transition diagram (state diagram for short) of the

Markov chain.

A node labeled i of the state diagram represents state i

of the Markov chain and a branch labeled pij from node i

to j implies that the conditional probability is

1[ | ]n n ijP X j X i p

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Example: Two states

Suppose a person can be in one of two states "healthy"

or "sick". Let X(n), n = 0, 1, 2, … refer the state at time n

where

Define

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Its corresponding DTMC can be shown by

state diagram

transition probability matrix

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We are interested in obtaining an expression for

evaluating the n-step transition probability from the one-

step probabilities.

If we let P(n) be the matrix whose (i, j) entry is , that

is, let P(n) be the matrix of n-step transition probabilities,

then we can write

Thus the matrix of n-step transition probabilities is

obtained by multiplying the matrix of one-step transition

probabilities by itself n-1 times.

( )ijp n

( ) . ( 1) nP n P P n P

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We can obtain the pmf of the random variable from

the n-step transition probabilities and the initial

probability vector as follows

This implies that step dependent probability vector of a

homogeneous Markov chain are completely determined

from the one-step transition probability matrix P and the

initial probability vector p(0).

p( ) p(0) ( ) p(0) nn P n P

nX

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Example: Stock Exchange

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Example: Stock Exchange…

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Example: Stock Exchange…

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A state i is said to be transient iff there is a positive

probability that the process will not return to this state.

A state i is said to be recurrent iff starting from i, the

process eventually returns to state i with probability one.

For a recurrent state i, define the period of

state i denoted by , as the greatest common divisor

(gcd) of the set of positive integers n such that .

( ) 0, 1iip n n

iid

( ) 0iip n

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A state i has period k if any return to state i must occur in

multiples of k time steps. Formally, the period of a state

is defined as

A recurrent state i is said to be aperiodic if its period ,

and periodic if .

A state i is said to be an absorbing state iff .

1id

1id

0gcd{ : Pr( | ) 0}nk n X i X i

1iip

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Two states i and j communicate if directed paths from i to

j and vice-versa exist.

A Markov chain is said to be irreducible if every recurrent

state can be reached from every other state in a finite

number of steps. In other words, for all , there is an

integer such that .

,i j I

1n ( ) 0ijp n

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Continuous Time Markov Chain (CTMC)

As in DTMCs, we confine our attention to discrete-state

processes. This implies that, although the parameter t

has a continuous range of values, the set of values is

discrete.

Recall the definition of discrete-state continuous tie

stochastic process stated in the class which satisfies

The behavior of the process is characterized by (1) initial

state probability given by the pmf of

and (2) the transition probabilities

( , ) ( ( ) | ( ) )ijp v t P X t j X v i

( )X t

1 1 0 0[ ( ) | ( ) , ( ) ,... ( ) ]n n n nP X t x X t x X t x X t x

[ ( ) | ( ) ]n nP X t x X t x

0 0( ), ( ( ) ), 0,1,2,...X t P X t k k

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Continuous Time Markov Chain (CTMC)…

Let denote the pmf of X(t) (or the state probabilities at

time t by

It is clear that

for any , since at any given time the process must be

in some state.

( ) ( ( ) ),j t P X t j 0,1,2,...; 0j t

( ) 1j

j I

t

0t

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Using the theorem of total probability, for given , we

can express the pmf of X(t) in term of the transition

probabilities and the pmf of X(v):

If we let v=0, then

( ) ( ( ) )j t P X t j

t v

( , )ijp v t

( ( ) | ( ) ) ( ( ) )i I

P X t j X v i P X v j

( , ) ( )ij i

i I

p v t v

( ) (0, ) (0)j ij i

i I

t p t

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If we let the , then in the matrix form

we have

Where Q is the infinitesmial generator matrix containing

the transition rates from any state i to any other state j,

where of a given CTMC.

The elements on the main diagonal of Q are defined by

( )( )

d tt Q

dt

0 1( ) [ ( ), ( ),...]t t t

i jijq

iiq

,

ii ij

j j i

q q

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If for a given CTMC, the steady state probabilities are

independent of time, we immediately get

For determining the unconditional state probabilities

resolves to much simpler system of linear equations

In matrix for, we get accordingly

( )lim 0t

d t

dt

0 ,ij i

i S

q j S

0 Q

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Example:

Discussion on steady state solution of the following CTMC in class…

1 2 3

1 2 3 1

Markov Chains (2) - Sharifce.sharif.edu/courses/93-94/1/ce634-1/resources/root... · 2020. 9....

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