Networking Theory and Fundamentals - Lecture 3 ppt

TCOM 501: Networking Theory & Fundamentals Lecture January 29, 2003 Prof Yannis A Korilis 3-2 Topics Markov Chains Discrete-Time Markov Chains Calculating Stationary Distribution Global Balance Equations Detailed Balance Equations Birth-Death Process Generalized Markov Chains Continuous-Time Markov Chains 3-3 Markov Chain Stochastic process that takes values in a countable set Example: {0,1,2,…,m}, or {0,1,2,…} Elements represent possible “states” Chain “jumps” from state to state Memoryless (Markov) Property: Given the present state, future jumps of the chain are independent of past history Markov Chains: discrete- or continuous- time 3-4 Discrete-Time Markov Chain Discrete-time stochastic process {Xn: n = 0,1,2,…} Takes values in {0,1,2,…} Memoryless property: P{ X n +1 = j | X n = i, X n −1 = in −1 , , X = i0 } = P{ X n +1 = j | X n = i} Pij = P{ X n +1 = j | X n = i} Transition probabilities Pij Pij ≥ 0, ∞ ∑P j =0 ij =1 Transition probability matrix P=[Pij] 3-5 Chapman-Kolmogorov Equations n step transition probabilities Pijn = P{ X n + m = j | X m = i}, n, m ≥ 0, i, j ≥ Chapman-Kolmogorov equations n+m ij P ∞ = ∑ Pikn Pkjm , n, m ≥ 0, i, j ≥ k =0 Pijn is element (i, j) in matrix Pn Recursive computation of state probabilities 3-6 State Probabilities – Stationary Distribution State probabilities (time-dependent) π nj = P{ X n = j}, ∞ π n = (π 0n , π1n , ) ∞ P{ X n = j} = ∑ P{ X n −1 = i}P{ X n = j | X n −1 = i} ⇒ π = ∑ π in −1Pij n j i =0 i =0 In matrix form: π n = π n −1P = π n −2 P = = π P n If time-dependent distribution converges to a limit π = lim π n n→∞ π is called the stationary distribution π = πP Existence depends on the structure of Markov chain 3-7 Classification of Markov Chains Irreducible: States i and j communicate: Aperiodic: State i is periodic: ∃n, m : Pijn > 0, Pjim > ∃ d > 1: Piin > ⇒ n = α d Irreducible Markov chain: all states communicate Aperiodic Markov chain: none of the states is periodic 2 0 4 3-8 Limit Theorems Theorem 1: Irreducible aperiodic Markov chain For every state j, the following limit π j = lim P{ X n = j | X = i}, i = 0,1, 2, n →∞ exists and is independent of initial state i Nj(k): number of visits to state j up to time k N j (k )  P  π j = lim k →∞ k   X0 = i =  πj: frequency the process visits state j 3-9 Existence of Stationary Distribution Theorem 2: Irreducible aperiodic Markov chain There are two possibilities for scalars: π j = lim P{ X n = j | X = i} = lim Pijn n →∞ πj = 0, for all states j πj > 0, for all states j n →∞ No stationary distribution π is the unique stationary distribution Remark: If the number of states is finite, case is the only possibility 3-10 Ergodic Markov Chains Markov chain with a stationary distribution π j > 0, j = 0,1, 2, States are positive recurrent: The process returns to state j “infinitely often” A positive recurrent and aperiodic Markov chain is called ergodic Ergodic chains have a unique stationary distribution π j = lim Pijn n →∞ Ergodicity ⇒ Time Averages = Stochastic Averages ... Numerically determine limit of Pn 0 .31 0 0 .34 5 0 .34 5 lim P n =  0 .31 0 0 .34 5 0 .34 5   n →∞ 0 .31 0 0 .34 5 0 .34 5 (n ≈ 150) Effectiveness depends on structure of P 3- 1 5 Global Balance Equations Markov... π + π1 + π = P{gets wet} = π p = p 1− p 3? ?? p 3- 1 4 Example: Finite Markov Chain Taking p = 0.1:  1− p 1  π= , , = ( 0 .31 0, 0 .34 5, 0 .34 5)   3? ?? p 3? ?? p 3? ?? p  1  P =  0.9 0.1   0.9 0.1... the chain are independent of past history Markov Chains: discrete- or continuous- time 3- 4 Discrete-Time Markov Chain Discrete-time stochastic process {Xn: n = 0,1,2,…} Takes values in {0,1,2,…}