Markov chains, imperfect state information, and Bayesian learning

Bayesian inference for Markov chains ∗

We consider the estimation of Markov transition matrices by Bayes’ methods. We obtain large and moderate deviation principles for the sequence of Bayesian posterior distributions. MSC 2000 subject classification: 60F10, 62M05

Pairwise Markov Chains and Bayesian Unsupervised Fusion

Learning Bayesian Network Structure using Markov Blanket in K2 Algorithm

‎A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG)‎. ‎There are basically two methods used for learning Bayesian network‎: ‎parameter-learning and structure-learning‎. ‎One of the most effective structure-learning methods is K2 algorithm‎. ‎Because the performance of the K2 algorithm depends on node...

Bayesian analysis for reversible Markov chains

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Bayesian Nonparametric Analysis of Reversible Markov Chains

We introduce a three-parameter random walk with reinforcement, called the (θ,α,β) scheme, which generalizes the linearly edge reinforced random walk to uncountable spaces. The parameter β smoothly tunes the (θ,α,β) scheme between this edge reinforced random walk and the classical exchangeable two-parameter Hoppe urn scheme, while the parameters α and θ modulate how many states are typically vis...