Markov Processes and Birth-Death Processes
Download

Markov Processes and Birth-Death Processes

Advertisement
Download Presentation
Comments
renardo
From:
|  
(108) |   (0) |   (0)
Views: 210 | Added: 25-03-2012
Rate Presentation: 2 1
Description:

Markov Processes and Birth-Death Processes

An Image/Link below is provided (as is) to

Download Policy: Content on the Website is provided to you AS IS for your information and personal use only and may not be sold or licensed nor shared on other sites. SlideServe reserves the right to change this policy at anytime. While downloading, If for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.











- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -




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.


Other Related Presentations

Copyright © 2014 SlideServe. All rights reserved | Powered By DigitalOfficePro