Appendix CMath
C.1 Martingales [102]
Martingale Definition
A stochastic process {Xn; n = 0, 1, …} is martingale if, for n = 0, 1, …,
- E[|Xn|] < ∞
- E[Xn+1|X0, …, Xn] = Xn
Def.: Let {Xn; n = 0, 1, …} and {Yn; n = 0, 1, …} be stochastic processes. We say {Xn} is martingale with respect to (w.r.t) {Yn} if, for n = 0, 1, …:
- E[|Xn|] < ∞
- E[Xn+1|Y0, …, Yn] = Xn
Examples of Martingales:
- Suns of independent random variables: Xn = Y1 + ⋯ + Yn.
- Variance of a Sum Xn = 2 − nσ2
- Have induced Martingales with Markov Chains! ….
- For HMM learning, sequences of likelihood ratios are martingale….
The asymptotic equipartition theorem (AEP) and Hoeffding Inequalities (critical in Chapter 11) have both been generalized to Martingales.
Induced Martingales with Markov Chains [102]
Let {Yn; n = 0, 1, …} be a Markov Chain (MC) process with transition probability matrix P = ||Pij||. Let f be a bounded right regular sequence for P:
f(i) is non‐negative and . Let Xn = f(Yn) ➔ E[|Xn|] < ∞ (since f is bounded). Now have:
In HMM Learning Have Sequences of Likelihood Ratios, Which Is a Martingale, Proof
Let Y0, Y1, … be iid rv.s and let f0 and f1 be probability density functions. A stochastic process of ...
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