Continuous Time Markov Chains Birth and Death Processes,Transition Probability Function, Kolmogorov Equations, Limiting Probabilities, Uniformization Chapter 6 1 Markovian Processes State Space Discrete Parameter Space (Time) Discrete Continuous Continuous Markov chains (Chapter 4) Continuous time Brownian Markov chains motion process (Chapters 5, 6) (Chapter 10) Chapter 6 2 Continuous Time Markov Chain A stochastic process {X(t), t 0} is a continuous time Markov chain (CTMC) if for all s, t 0 and nonnegative integers i, j, x(u), 0 u < s, P X s t j X s i, X u x u , 0 u s P X s t j X s i and if this probability is independent of s, then the CTMC has stationary transition probabilities: Pij t P X s t j X s i for all s Chapter 6 3 Alternate Definition Each time the process enters state i, The amount of time it spends in state i before making a transition to a different state is exponentially distributed with parameter vi, and When it leaves state i, it next enters state j with probability Pij, where Pii = 0 and j Pij 1 Let qij vi Pij , then vi j qij , Pij h 1 Pii h lim vi and lim qij h 0 h 0 h h Chapter 6 4 Birth and Death Processes If a CTMC has states {0, 1, …} and transitions from state n may go only to either state n - 1 or state n + 1, it is called a birth and death process. The birth (death) rate in state n is ln (mn), so v0 l0 vi li mi , i 0 P01 1 Pi ,i 1 lo 0 1 m1 li li mi l1 m2 , Pi ,i 1 mi li mi 2 ,i 0 ln-1 n-1 Chapter 6 ln n mn n+1 mn+1 5 Chapman-Kolmogorov Equations “In order to get from state i at time 0 to state j at time t + s, the process must be in some state k at time t” Pij t s Pik t Pkj s k 0 From these can be derived two sets of differential equations: “Backward” Pij t qik Pkj t vi Pij t k i “Forward” Pij t qkj Pik t v j Pij t k j Chapter 6 6 Limiting Probabilities If • All states of the CTMC communicate: For each pair i, j, starting in state i there is a positive probability of ever being in state j, and • The chain is positive recurrent: starting in any state, the expected time to return to that state is finite, then limiting probabilities exist: Pj lim Pij t t (and when the limiting probabilities exist, the chain is called ergodic) Can we find them by solving something like p = p P for discrete time Markov chains? Chapter 6 7 Infinitesimal Generator (Rate) Matrix qij , if i j Let R be a matrix with elements rij vi , if i j (the rows of R sum to 0) Let t in the forward equations. In steady state: lim Pij t lim qkj Pik t v j Pij t t t k j 0 qkj Pk v j Pj k j These can be written in matrix form as PR = 0 along with j Pj 1 and solved for the limiting probabilities. What do you get if you do the same with the backward equations? Chapter 6 8 Balance Equations The PR = 0 equations can also be interpreted as balancing: v j Pj qkj Pk k j rate at which process leaves j rate at which process enters j For a birth-death process, they are equivalent to levelcrossing equations ln Pn mn1Pn1 rate of crossing from n to n 1 rate of crossing from n 1 to n so P l0 l1 ln 1 P and a steady state exists if n 0 l0l1 ln1 m1m 2 m n mm n 1 Chapter 6 1 2 mn 9 Time Reversibility A CTMC is time-reversible if and only if Pq i ij Pj q ji when i j There are two important results: 1. An ergodic birth and death process is time reversible 2. If for some set of numbers {Pi}, i Pi 1 and Pq i ij Pj q ji when i j then the CTMC is time-reversible and Pi is the limiting probability of being in state i. This can be a way of finding the limiting probabilities. Chapter 6 10 Uniformization Before, we assumed that Pii = 0, i.e., when the process leaves state i it always goes to a different state. Now, let v be any number such that vi v for all i. Assume that all transitions occur at rate v, but that in state i, only the fraction vi/v of them are real ones that lead to a different state. The rest are fictitious transitions where the process stays in state i. Using this fictitious rate, the time the process spends in state i is exponential with rate v. When a transition occurs, it goes to state j with probability vi 1 , j i v * Pij vi P , j i v ij Chapter 6 11 Uniformization (2) In the uniformized process, the number of transitions up to time t is a Poisson process N(t) with rate v. Then we can compute the transition probabilities by conditioning on N(t): Pij t P X t j X 0 i P X t j X 0 i, N t n P N t n X 0 i n 0 e vt vt P X t j X 0 i, N t n n! n 0 e vt vt Pij n! n 0 n n *n Chapter 6 12 More on the Rate Matrix Can write the backward differential equations as P t RP t and their solution is P t P 0 eRt eRt since P 0 I n where Rt t n e R n 0 n! but this computation is not very efficient. We can also approximate: t e lim I R n n Rt n t or e I R n Rt Chapter 6 1 n for large n 13