Download e-book for iPad: Interactive Markov Chains: And the Quest for Quantified by Holger Hermanns (eds.)

By Holger Hermanns (eds.)

Markov Chains are widespread as stochastic types to check a large spectrum of approach functionality and dependability features. This monograph is dedicated to compositional specification and research of Markov chains.
Based on ideas recognized from strategy algebra, the writer systematically develops an algebra of interactive Markov chains. via providing a few distinguishing effects, of either theoretical and functional nature, the writer substantiates the declare that interactive Markov chains are greater than simply one other formalism: between different, an algebraic conception of interactive Markov chains is built, devise algorithms to mechanize compositional aggregation are awarded, and country areas of numerous million states as a result of the examine of a regular telefone procedure are analyzed.

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To illustrate this we have, again, shaded bisimilar states with the same pattern. A crucial aspect is that the weak τ =⇒ of the right process can be simulated by the internal transition 111 000 000 111 000 111 000 111 τ 000 000 the reflexive closure. 111 left process because111 =⇒ contains This example shows that weak bisimilarity is quite an appropriate notion to compare the behaviour of components, where some actions have been internalised. Furthermore, substitutivity ensures that equivalent components can be exchanged by each other inside a larger composition context without affecting the behaviour of the composite process.

They will also be crucial for many explanations in later chapters. (A) An exponential distribution P rob{delay ≤ t} = 1 − e−λt is characterised by a single parameter λ, a positive real value, usually referred to as the rate of the distribution. The mean duration of this delay amounts to 1/λ time units. (B) In correspondence to geometric distributions in the discrete-time setting, the class of exponential distribution is the only class of memoryless continuous probability distribution. The remaining delay after some time t0 has elapsed is a random variable with the same distribution as the whole delay: P rob{delay ≤ t + t0 | delay > t0 } = P rob{delay ≤ t}.

T0 (of arbitrary length n), we have that for each (measurable) subset A of states, P rob{Xtn+1 ∈ A | Xtn = Pn , Xtn−1 = Pn−1 , Xtn−2 = Pn−2 , . . 1) = P rob{Xtn+1 ∈ A | Xtn = Pn }. Thus, the fact that the process was in state Pn−1 at time tn−1 , in state Pn−2 at time tn−2 , and so on, up to the fact that it was in state P0 at time t0 is entirely irrelevant. The state Xtn contains all relevant history information to determine the random distribution on S at time tn+1 . A. Markov, who studied processes with this property at the beginning of the last century [137].

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