# Introduction to Bayesian Network and Influence Diagram by hcj

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Continuous-Time
Markov Chains

Nur Aini Masruroh
LOGO
Introduction
 A continuous-time Markov chain is a stochastic process
having the Markovian property that the conditional
distribution of the future state at time t+s, given the
present state at s and all past states depends only on
the present state and is independent of the past.
 If {X(t+s) = j|X(s) = i} is independent of s, then the
continuous-time Markov chain is said to have stationary
or homogeneous transition probabilities.
 All Markov chain we consider to have stationary
transition probabilities
LOGO
Properties
 Suppose that a continuous-time Markov chain enters state i and the
process does not leave state i (transition does not occur) during the
next s time units. What is the probability that the process will not
leave state i during the following t time units?

 The process is in state i at time s, by Markovian property, the
probability it remains in that state during the interval [s, s+t] is just
the unconditional probability that it stays in state i for at least t time
units.
 If τi denotes the amount of time that the process stays in state i
before making a transition into a different state, then P{τi > s+t|τi
>s} = P{τi > t} for all s, t ≥ 0.
 the random variable τi is memoryless and must thus be
exponential distributed
LOGO
Properties
 Based on the previous property, a continuous-time
Markov chain is a stochastic process having the
properties that each time it enters state i:
 The amount of time it spends in that state before
making a transition into a different state is
exponentially distributed with rate vi, 0 ≤ vi < ∞
• If vi = ∞  instantaneous state, when entered it is
instantaneously left
• If vi = 0  absorbing state
 When the process leaves state i, it will next enter
state j with some probability Pij where  j i Pij  1
LOGO
Properties
 A continuous-time Markov chain is a stochastic process
that moves from state to state in accordance with a
discrete-time Markov chain, but it such that the amount
of time it spends in each state, before proceeding to the
next state, is exponentially distributed
 The amount of time the process spends in state i and
the next state visited must be independent random
variables
 If the next state visited were dependent on τi then
information as how long the process has already been in
state i would be relevant to the prediction of the next
state  would contradict to the Markov assumption
LOGO
Properties
 A continuous-time Markov chain is regular if the number
of transitions in any finite length of time is finite
 Let qij = viPij for all i ≠ j
 Thus qij is the rate at when in state i that the process
makes a transition into state j  qij is the transition
rate from i to j
 If Pij(t) is the probability that a Markov chain presently in
state i will be in state j after an additional time t,
Pij(t) = P{X(t+s) = j|X(s) = i}
LOGO
Birth and Death Process
 An important class in continuous-time Markov chain
 Birth and death process is a continuous-time Markov chain with
states 0, 1, … for which qij = 0 whenever |i – j|> 1
 The transition from state i can only go either state i-1 or state i+1
 The state of the process is usually thought of as representing
the size of some population
• Increase by 1  birth occurs
• Decrease by 1  death occurs
 Denote:
λi = qi, i+1  birth rate
μi = qi, i-1  death rate
LOGO
Birth and death process
 Since

   j
qij  vi
vi  i  i
i
Pi ,i 1               1  Pi ,i 1
i  i

 Hence we think of a birth and death process by
supposing that whenever there are i people in the
system, the time until the next birth is exponential with
rate λi and is independent of the time until the next death
which is exponential with rate μi
LOGO
Example: two birth and death process

 The M/M/s queue
 Customers arrive at an s-server service station with Poisson
process having rate λ
 The service times are assumed to be independent exponential
random variables with mean 1/μ
 If X(t) denote the number in the system at tie t, then {X(t), t ≥ 0} is a
birth and death process with

 n 1  n  s
n  
 s n  s
n   , n  0
LOGO                 Linear growth model
with immigration
 Occur naturally in the study of biological reproduction and
population growth
 Each individual in the population is assumed to give birth at an
exponential rate λ
 There is an exponential rate of increase θ of the population due to
an external source such as immigration
 Deaths are assumed to occur at an exponential rate μ for each
number of population

n  n , n  1
n  n   , n  0
LOGO
Pure birth process

 The birth and death process is said to be pure birth
process if μn = 0 for all n (the death is impossible)
 The simplest example of pure birth process is Poisson
process, which has a constant birth rate λn = λ, n ≥ 0
 Yule process: a pure birth process which each member
acts independently and gives birth at an exponential rate
λ and no one ever dies.
 If X(t) represent the population size at time t, {X(t),
t≥0} is a pure birth process with λn = nλ, n ≥ 0
LOGO
Limiting probabilities
 Since a continuous-time Markov chain is a semi-
Markov process with Fij(t) = 1 – e-vit
 The limiting probabilities are given by
 j vj
Pj           , where j    i Pij and   i  1
  i vi
i
i             i

Thus
v j Pj   vi Pi Pij and  Pj  1
i                 j

or, using qij  vi Pij
v j Pj   Pi qij and  Pj  1
i             j
LOGO          Limiting probabilities for the
Birth and Death Process
Rate In = Rate Out Principle
 For any state of the system n, the mean rate at which
the entering incidents occurs must equal the mean
rate at which the leaving incidents.
LOGO
Balance equation
The equations for the rate diagram can be formulated as
follows:
State 0: μ1p1 = λ0 p0
State 1: λ0 p0 + μ2p2 = (λ1 + μ1)p1
State 2: λ1 p1 + μ3p3 = (λ2 + μ2)p2
….
State n: λn-1 pn-1 + μn+1 pn+1 = (λn+ μn)pn
….
LOGO
Balance equation (cont’d)
0
State 0 : p1      p0
1

State 1 : p2  1 0 p0
 2 1
 
State 2 : p3  2 1 0 p0
3  2 1

n 1n  2  0
State n : pn                     p0
 n  n 1  1

1

n 1n  2  0              
   n 1 
Using n 0 pn  1 we obtain 1  p0  p0                         or p0  1   0 1


n 1  n  n 1  1              n 1 1  2   n 

0 1  n 1                                        Should be < ∞
and hence pn                                                                Limiting probabilities to exist
  0 1  n 1 
1 2   n  
                   

 n 1 1 2   n 
LOGO
Example: job-shop problem

 Consider a job-shop consisting M machines and a single
repairman. Suppose the amount of time a machine runs
before breaking down is exponentially distributed with
rate λ and the amount of time it takes the repairman to
fix any broken machine is exponential with rate μ. If we
say that the state is n whenever there are n machines
down, then this system can be modeled as a birth and
death process with parameters
n   , n  1
( M  n)   nM
n  
0           nM
LOGO             Example: job-shop problem
(cont’d)
 The limiting probability that n machines will not be in use, pn is
defined as
1
p0                     n
M
     M!
1    
n 1    M  n !
n
M!   
 
M  n !   
 
pn                     n
, n  0,, M
M
     M!
1    
n 1    M  n !

 The average number of machines not in use is given by
n
M
M!   
M                 n M  n!   
 
 
 np     n       n 0
n
M
     M!
1  
n 0
 
n 1    M  n !
LOGO      Example: job-shop problem
(cont’d)
 Suppose we want to know the long-run proportion of
time that a given machine is working
 The equivalent limiting probability of its working:
p{machine is working}
M
  P{machine is working | n not working}pn
n 0
M
M n
         p0
n 0  M
LOGO
Time reversibility
 Consider the continuous-time Markov process going
backwards in time
 Since the forward process is continuous-time Markov
chain it follows that given the present state, X(t), the past
state X(t – s) and the future state X(y), y > t are
independent
 Therefore P{X(t-s) = j| X(t) = i, X(y), y>t}
= P{X(t-s) = j|X(t) = i}
 So, the reverse process is also the continuous-time
Markov chain
LOGO
Time reversibility (cont’d)

 Since the amount of time spent in a state is the same whether one
is going forward or backward in time, it follows that the amount of
time reverse chain spends in state i on a visit is exponential with the
same rate vi as in the forward process
 Suppose the process is in state i at time t, the probability that its
backward time in i exceeds s is

P{processis in state i throughout[t  s, t ] | X (t )  i}
 P{processis in state i throughout[t  s, t ]} / P{ X (t )  i}
P{ X (t  s)  i}e vi s

P{ X (t )  i}
 e vi s
Since P{ X (t  s)  i}  P{ X (t )  i}  Pi
LOGO
Time reversibility (cont’d)

 The sequence of states visited by the reverse process
constitutes a discrete-time Markov chain with transition
probabilities Pij* given by
Pij* = πjPji/ πi (condition for time reversibility)
where (πj, j ≥0) are the stationary probabilities of the
embedded discrete-time Markov chain with transition
probability Pij
 The condition of time reversibility:
the rate at which the process goes directly from state i to
state j is equal to the rate at which it goes directly from j
to i
LOGO

So, can you differentiate among Discrete-Time Markov
Chain, Discrete-Time Semi Markov Chain, and Continuous-
Time Markov Chain now?

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