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Markov Processes and Birth-Death Processes

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**1. **Markov Processes and Birth-Death Processes
J. M. Akinpelu

**2. **Exponential Distribution Definition. A continuous random variable X has an exponential distribution with parameter ? > 0 if its probability density function is given by
Its distribution function is given by

**3. **Exponential Distribution Theorem 1. A continuous R.V. X is exponentially distributed if and only if for
or equivalently,
A random variable with this property is said to be memoryless.

**4. **Exponential Distribution Proof: If X is exponentially distributed, (1) follows readily. Now assume (1). Define F(x) = P{X = x}, f (x) = F?(x), and, G(x) = P{X > x}. It follows that G?(x) = ? f (x). Now fix x. For h ? 0,
This implies that, taking the derivative wrt x,

**5. **Exponential Distribution Letting x = 0 and integrating both sides from 0 to t gives

**6. **Exponential Distribution Theorem 2. A R.V. X is exponentially distributed if and only if for h ? 0,

**7. **Exponential Distribution Proof: Let X be exponentially distributed, then for h ? 0,
The converse is left as an exercise.

**8. **Exponential Distribution

**9. **Markov Process A continuous time stochastic process {Xt, t ? 0} with state space E is called a Markov process provided that
for all states i, j ? E and all s, t ? 0.

**10. **Markov Process We restrict ourselves to Markov processes for which the state space E = {0, 1, 2, …}, and such that the conditional probabilities
are independent of s. Such a Markov process is called time-homogeneous.
Pij(t) is called the transition function of the Markov process X.

**11. **Markov Process - Example Let X be a Markov process with
where
for some ? > 0. X is a Poisson process.

**12. **Chapman-Kolmogorov Equations Theorem 3. For i, j ? E, t, s ? 0,

**13. **Realization of a Markov Process

**14. **Time Spent in a State Theorem 4. Let t ? 0, and n satisfy Tn = t < Tn+1, and let Wt = Tn+1 – t. Let i ? E, u ? 0, and define
Then
Note: This implies that the distribution of time remaining in a state is exponentially distributed, regardless of the time already spent in that state.

**15. **Time Spent in a State Proof: We first note that due to the time homogeneity of X, G(u) is independent of t. If we fix i, then we have

**16. **An Alternative Characterization of a Markov Process Theorem 5. Let X ={Xt, t ? 0} be a Markov process. Let T0, T1, …, be the successive state transition times and let S0, S1, …, be the successive states visited by X. There exists some number ?i such that for any non-negative integer n, for any j ? E, and t > 0,
where

**17. **An Alternative Characterization of a Markov Process This implies that the successive states visited by a Markov process form a Markov chain with transition matrix Q.
A Markov process is irreducible recurrent if its underlying Markov chain is irreducible recurrent.

**18. **Kolmogorov Equations Theorem 6.
and, under suitable regularity conditions,
These are Kolmogorov’s Backward and Forward Equations.

**19. **Kolmogorov Equations Proof (Forward Equation): For t, h ? 0,
Hence
Taking the limit as h ? 0, we get our result.

**20. **Limiting Probabilities Theorem 7. If a Markov process is irreducible recurrent, then limiting probabilities
exist independent of i, and satisfy
for all j. These are referred to as “balance equations”. Together with the condition
they uniquely determine the limiting distribution.

**21. **Birth-Death Processes Definition. A birth-death process {X(t), t ? 0} is a Markov process such that, if the process is in state j, then the only transitions allowed are to state j + 1 or to state j – 1 (if j > 0).
It follows that there exist non-negative values ?j and ?j,
j = 0, 1, 2, …, (called the birth rates and death rates) so that,

**22. **Birth and Death Rates

**23. **Differential-Difference Equations for a Birth-Death Process It follows that, if , then
Together with the state distribution at time 0, this completely describes the behavior of the birth-death process.

**24. **Birth-Death Processes - Example Pure birth process with constant birth rate
?j = ? > 0, ?j = 0 for all j. Assume that
Then solving the difference-differential equations for this process gives

**25. **Birth-Death Processes - Example Pure death process with proportional death rate
?j = 0 for all j, ?j = j? > 0 for 1 = j = N, ?j = 0 otherwise, and
Then solving the difference-differential equations for this process gives

**26. **Limiting Probabilities Now assume that limiting probabilities Pj exist. They must satisfy:
or

**27. **Limiting Probabilities These are the balance equations for a birth-death process. Together with the condition
they uniquely define the limiting probabilities.

**28. **Limiting Probabilities From (*), one can prove by induction that

**29. **When Do Limiting Probabilities Exist? Define
It is easy to show that
if S < ?. (This is equivalent to the condition P0 > 0.) Furthermore, all of the states are recurrent positive, i.e., ergodic. If S = ?, then either all of the states are recurrent null or all of the states are transient, and limiting probabilities do not exist.

**30. **Flow Balance Method Draw a closed boundary around state j:
“flow in = flow out”

**31. **Flow Balance Method Draw a closed boundary between state j and state j–1:

**32. **Example Machine repair problem. Suppose there are m machines serviced by one repairman. Each machine runs without failure, independent of all others, an exponential time with mean 1/?. When it fails, it waits until the repairman can come to repair it, and the repair itself takes an exponentially distributed amount of time with mean 1/?. Once repaired, the machine is as good as new.
What is the probability that j machines are failed?

**33. **Let Pj be the steady-state probability of j failed machines.
Example

**34. **Example

**35. **Example How would this example change if there were m (or more) repairmen?

**36. **Homework No homework this week due to test next week.

**37. **References Erhan Cinlar, Introduction to Stochastic Processes, Prentice-Hall, Inc., 1975.
Leonard Kleinrock, Queueing Systems, Volume I: Theory, John Wiley & Sons, 1975.
Sheldon M. Ross, Introduction to Probability Models, Ninth Edition, Elsevier Inc., 2007.