Cycle Representations of Markov Processes by Sophia L. Kalpazidou

By Sophia L. Kalpazidou

This publication is a prototype delivering new perception into Markovian dependence through the cycle decompositions. It offers a scientific account of a category of stochastic approaches often called cycle (or circuit) procedures - so-called simply because they're outlined by means of directed cycles. those methods have unique and critical homes in the course of the interplay among the geometric houses of the trajectories and the algebraic characterization of the Markov procedure. a big software of this method is the perception it presents to electric networks and the duality precept of networks. particularly, it offers a completely new method of countless electric networks and their functions in themes as varied as random walks, the category of Riemann surfaces, and to operator theory.

The moment version of this e-book provides new advances to many instructions, which show wide-ranging interpretations of the cycle representations like homologic decompositions, orthogonality equations, Fourier sequence, semigroup equations, and disintegration of measures. the flexibility of those interpretations is therefore inspired by means of the life of algebraic-topological ideas within the basics of the cycle representations. This booklet comprises bankruptcy summaries in addition to a couple of distinct illustrations.

Review of the sooner edition:

"This is a truly necessary monograph which avoids prepared methods and opens new study views. it's going to definitely stimulate extra paintings, specifically at the interaction of algebraic and geometrical points of Markovian dependence and its generalizations."

Math Reviews.

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Weak Convergence of Sequences of Circuit Chains N ow consider any i, j in S such that Pij > 0. Then there exists (J that i, j are interior points of B( 0, n,,) and (i, j) E Arcset "C{1. p'1.. k. = "L.... r=l ~ 1 such n·wer Jer (i ' J') , and L nswc J k. nsw(i, j) = r=1 c r (i, j), r for all s ~ (J. Finally, we have = L wJc(i, j). CE'" o The proof is complete. Remark. , id defined by w(i,j) = niPij, i,j E S. 3. Let fl and f2 be two positive functions defined on S2. In order that equations 'I,fl(i,j) = 'I,f2(j,i), iES, j j be circuit-generating ones it is necessary that for so me i l , ...

4. Weak Convergence of Sequences of Circuit Chains: A Deterministic Approach We introduced two types of circuit representations of Markov chains according to whether or not the corresponding algorithms define the circuit-weights by a random or a nonrandom choice. In the spirit of Kolmogorov we may call such algorithms probabilistic (randomized) and deterministic (nonrandomized) algorithms, respectively. 1 is generalized to infinite classes of directed circuits such that the corresponding denumerable circuit Markov chain ~ can be defined as a limit of a certain sequence (mOm of finite circuit chains.

For instance, if such a history is k = b, we are interested in defining the transition probabilities from e n = k to en+l = x, x E S, where n belongs to the set Z of all integers. Thus, to calculate these transition probabilities we en' 17 2. 1. follow the steps below: (i) We look for the set CC(k) of all circuits which pass through k. In case CC(k) is not empty, then the passages to other states are allowed and we may go on with the following steps. (ii) We consider the set CC(k, x) of all circuits which pass through (k, x).

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