Resolving Markov chains onto Bernoulli shifts via positive polynomials
著者
書誌事項
Resolving Markov chains onto Bernoulli shifts via positive polynomials
（Memoirs of the American Mathematical Society, no. 710）
American Mathematical Society, 2001
大学図書館所蔵 件 / 全16件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
"Volume 150, number 710 (first of 5 numbers)"
Includes bibliographical references
内容説明・目次
内容説明
The two parts of this Memoir contain two separate but related papers. The longer paper in Part A obtains necessary and sufficient conditions for several types of codings of Markov chains onto Bernoulli shifts. It proceeds by replacing the defining stochastic matrix of each Markov chain by a matrix whose entries are polynomials with positive coefficients in several variables; a Bernoulli shift is represented by a single polynomial with positive coefficients, $p$. This transforms jointly topological and measure-theoretic coding problems into combinatorial ones. In solving the combinatorial problems in Part A, we state and make use of facts from Part B concerning $p^n$ and its coefficients. Part B contains the shorter paper on $p^n$ and its coefficients, and is independent of Part A. An announcement describing the contents of this Memoir may be found in the Electronic Research Announcements of the AMS at the following Web address ams.org/era.
目次
Part A. Resolving Markov Chains onto Bernoulli Shifts: Introduction Weighted graphs and polynomial matrices The main results Markov chains and regular isomorphism Necessity of the conditions Totally conforming eigenvectors and the one-variable case Splitting the conforming eigenvector in the one-variable case Totally conforming eigenvectors for the general case Splitting the conforming eigenvector in the general case Bibliography Part B. On Large Powers of Positive Polynomials in Several Variables: Introduction Structure of ${\mathbf{Log}}({\mathbf(p}^{\mathbfn})$ Entropy and equilibrium distributions for ${\mathbfw}\in {\mathbfW}({\mathbfp})$ Equilibrium distributions and coefficients of ${\mathbf(p}^{\mathbfn})$ Proofs of the estimates Bibliography.
「Nielsen BookData」 より
