Titlebook: Constructive Computation in Stochastic Models with Applications; The RG-Factorization Quan-Lin Li Book 2010 The Editor(s) (if applicable) a

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書目名稱Constructive Computation in Stochastic Models with Applications影響因子(影響力)學(xué)科排名




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書目名稱Constructive Computation in Stochastic Models with Applications讀者反饋




書目名稱Constructive Computation in Stochastic Models with Applications讀者反饋學(xué)科排名

積云

Block-Structured Markov Chains,me or continuous-time. The .- and .-measures are iteratively defined from two different censored directions: UL-type and LU-type. An important censoring invariance for the .- and .-measures is obtained. Using the censoring invariance, the Wiener-Hopf equations are derived, and then the UL- and UL-ty

組裝

Markov Chains of GI/G/1 Type,he repeated blocks and the four basic inequalities for the boundary blocks, which are useful in spectral analysis of Markov chains of ./1 type. We analyze the dual Markov chain of any irreducible blockstructured Markov chain, and specifically discuss the dual chain of a Markov chain of ./1 type. Fur

MEET

Asymptotic Analysis, under which the stationary probability vector is either light-tailed or heavy-tailed by means of the .-factorization. At the same time, we provide expressions for both the light tail and the heavy tail. Note that the conditions and expressions can be completely determined by the repeating row and t

清真寺

生來

生來

Examples of Practical Applications,nder a unified algorithmic framework. The processor-sharing queue is directly constructed as a block-structured Markov chain, and the fluid queue can be constructed as a block-structured Markov chain by means of the Laplace transform; while the negative-customer queue and the retrial queue need to c

借喻

Mnemonics

Quasi-Stationary Distributions, We derive the .-factorizations for the β -discounted block-structured transition matrix. We provide conditions for the state α -classification, and derive two sets of expressions for the quasi-stationary distribution. As important examples, we analyze Markov chains of ./1 type, Markov chains of ./1

孤僻

Markov Reward Processes,ide a unified algorithmic framework to derive expressions for conditional distributions and conditional moments of the reward processes. As an important example, we study the reward processes for an irreducible continuous-time level-dependent QBD process with either finitely-many levels or infinitel