Cycle Representations of Markov Processes by Sophia L. Kalpazidou

By Sophia L. Kalpazidou

This ebook provides an unique and systematic account of a category of stochastic methods often called cycle (or circuit) techniques, so referred to as simply because they're outlined through directed cycles. those procedures have specified and demanding houses during the interplay among the geometric homes of the trajectories and the algebraic characterization of the finite-dimensional distributions. a big software of this technique is the hot perception it presents into Markovian dependence and electric networks. particularly, it offers a completely new method of Markov procedures and endless electric networks, and their functions in subject matters as diversified as random walks, ergodic idea, dynamical structures, power conception, thought of matrices, algebraic topology, complexity concept, the class of Riemann surfaces, and operator theory.The writer surveys the 3 relevant advancements in cycle thought: the cycle-decomposition formulation and its relation to the Markov strategy; entropy construction and the way it can be used to degree how some distance a procedure is from being reversible; and the way a finite recurrent stochastic matrix will be outlined through a rotation of the circle and a partition whose parts include finite unions of circle-arcs. The cycle representations were complex after the booklet of the 1st variation to many instructions, which show wide-ranging interpretations like homologic decompositions, orthogonality equations, Fourier sequence, semigroup equations, disintegration of measures, etc. the flexibility of those interpretations is therefore encouraged through the lifestyles of algebraic-topological rules within the basics of the cycle representations,which elaborates the traditional view at the Markovian modelling to new intuitive and confident techniques.

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J Therefore any Markov chain determines a stochastic matrix. The converse, which is much deeper, is given by the well-known existence theorem of Kolmogorov and establishes a basic relationship between nonnegative matrices and Markov chains. The reader may find a comparative study of nonnegative matrices and Markov chains in E. Seneta (1981). 1, that is, their finite-dimensional distributions are completely determined by collections of weighted directed circuits. This will then motivate the definition and the general study of the Markovian dependence in terms of collections (C, wc ) of directed circuits and weights, which in turn leads to a link between nonnegative matrices and (C, wc ).

Ir , i1 (or any cyclic permutation). 2 or k(ω) is passed by a circuit completed after time n on the sample path (ξn (ω)). 2) σn (ω; i, j) = n cˆ∈Cn (ω) where εn (ω; i, j) = 1{the last occurrence of (i, j) does not happen (ω). 2) converges to πi pij and each summand of the right side converges to wc Jc (i, j). s. 1). 1) as in the following theorem due to Y. Derriennic (1993). 2. Let S be a denumerable set and let P = (pij , i, j ∈ S) be any stochastic matrix defining an irreducible and positive-recurrent Markov chain ξ.

In case C(k, x) is empty then no passage to x will take place. (iii) The transition probabilities from k(=b) to x are expressed in terms of the weights of the circuits in C(k) and C(k, x) by the relations: P(ξn+1 = d/ξn = b) = wc′ / c′ ∈C(b,d) wc′ c′ ∈C(b) = (wc1 + wc3 )/(wc1 + wc2 + wc3 ), P(ξn+1 = c/ξn = b) = wc′ / c′ ∈C(b,c) P(ξn+1 wc′ c′ ∈C(b) = wc2 /(wc1 + wc2 + wc3 ), = x/ξn = b) = 0, x ∈ S\{c, d}, for all n ∈ Z. Then the above probability law leads us to a Markov chain ξ = (ξn )n∈Z which will be called a circuit chain.

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