How do MCMC methods utilize Markov chains for sampling? What are some applications of MCMC methods?

This paper studies the existence of expectations equilibria for message processes on stochastic exchange environments. A message process which permits the ... Probability in the Engineering and Informational Sciences, vol. 12, 1998 ... BOOK: Stochastic-Process Limits, published by Springer in 2002 (602 pages). Let ξ , η be two random functions (= stationary (generalized) Gaussian random processes). The Gelfand–Pinsker–Yaglom functional (Formula (2.58) from [10]) for ... The dynamic features are governed by the information flow, modeled as a stochastic volatility process, and generalize standard ARCH specifications. The first one for obtaining concentration of Markov chains is to establish transport- entropy inequalities in the process level. This was begun again by Marton ... ... stochastic process, whether the original process is Markovian or not. ... information sharing called "informational coherence". Informational coherence, in ... ▷ Evolves according to a persistent stochastic process. ▷ Distribution πt|t ... ▷ Informational feedback onto CB information. L'Huillier and Zame ... Jul 13, 2022 ... This paper proposes a novel model of sequential information design, namely the Markov persuasion processes (MPPs), in which a sender, with informational ... Mar 6, 2020 ... Let Π be a fully connected transition operator for a Markov process on S, where. Π(s, s0) =: πs(s0) is the probability of transitioning from ... ... information in a random process and characterizing its asymptotic behavior. ... stochastic manner. The basic approach to proving positive coding theorems.