By G. George Yin, Qing Zhang

ISBN-10: 1461443458

ISBN-13: 9781461443452

ISBN-10: 1461443466

ISBN-13: 9781461443469

Prologue and Preliminaries: creation and evaluate- Mathematical preliminaries.- Markovian models.- Two-Time-Scale Markov Chains: Asymptotic Expansions of suggestions for ahead Equations.- profession Measures: Asymptotic homes and Ramification.- Asymptotic Expansions of recommendations for Backward Equations.- Applications:MDPs, Near-optimal Controls, Numerical tools, and LQG with Switching: Markov selection Problems.- Stochastic keep an eye on of Dynamical Systems.- Numerical equipment for regulate and Optimization.- Hybrid LQG Problems.- References.- Index

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Download e-book for iPad: Continuous-time Markov chains and applications : a by G. George Yin, Qing Zhang

Prologue and Preliminaries: creation and review- Mathematical preliminaries. - Markovian versions. - Two-Time-Scale Markov Chains: Asymptotic Expansions of strategies for ahead Equations. - profession Measures: Asymptotic houses and Ramification. - Asymptotic Expansions of suggestions for Backward Equations.

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2) If pε (t) converges, how can one determine the limit? (3) What is the convergence rate? (4) Suppose pε (t) → ν(t) = (ν1 (t), . . , νm (t)), a probability distribution as ε → 0. Define χε (t) = (I{αε (t)=1} , . . , I{αε (t)=m} ). Consider the centered and scaled occupation measure 1 nε (t) = √ ε t 0 (χε (s) − ν(s))ds. As ε → 0, what is the limit distribution of the random process nε (·)? (5) Will the results carry over to singularly perturbed Markov chains with weak and strong interactions (when the states of the Markov chain belong to multiple irreducible classes)?

The jump time τl+1 has the conditional probability distribution P (τl+1 − τl ∈ Bl |τ1 , . . , τl , α(τ1 ), . . , α(τl )) t+τl exp = Bl τl qα(τl )α(τl ) (s)ds −qα(τl )α(τl ) (t + τl ) dt. 22 2. Mathematical Preliminaries The post-jump location of α(t) = j, j = α(τl ) is given by P (α(τl+1 ) = j|τ1 , . . , τl , τl+1 , α(τ1 ), . . , α(τl )) = qα(τl )j (τl+1 ) . 5. Suppose that the matrix Q(t) satisfies the q-Property for t ≥ 0. Then (a) The process α(·) constructed above is a Markov chain. 4) 0 is a martingale for any uniformly bounded function f (·) on M.

3) p0i = 1, i=1 where ε > 0 is a small parameter and T < ∞. 3) can be written explicitly as pε (t) = p0 exp Qt ε . 1 Introduction 9 has a unique positive solution ν = (ν1 , . . , νm ), which is the stationary distribution of the Markov chain generated by Q. In addition, p0 exp(Qt) → ν as t → ∞. Therefore, for each t > 0, pε (t) converges to ν, as ε → 0 because t/ε → ∞. , κ0 t pε (t) − ν = O exp − for some κ0 > 0. ε Much of our effort in this book concerns obtaining asymptotic properties of pε (·), when the generator Q is a function of time.

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Continuous-time Markov chains and applications : a two-time-scale approach by G. George Yin, Qing Zhang


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