How many times should a card shuffler shuffle to get the cards shuffled? Convergence rate questions like these are central to the theory of finite Markov Chains and arise in diverse fields including Physics, Computer Science as well as Biology. This thesis introduces two new approaches to estimating mixing times: robust mixing time of a Markov Chain and Markovian product of Markov Chains. The “...

How many times should a card shuffler shuffle to get the cards shuffled? Convergence rate questions like these are central to the theory of finite Markov Chains and arise in diverse fields including Physics, Computer Science as well as Biology. This thesis introduces two new approaches to estimating mixing times: robust mixing time of a Markov Chain and Markovian product of Markov Chains. The “...

Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augme...

Heyman gives an interesting factorization of I − P , where P is the transition probability matrix for an ergodic Markov Chain. We show that this factorization is valid if and only if the Markov chain is recurrent. Moreover, we provide a decomposition result which includes all ergodic, null recurrent as well as the transient Markov chains as special cases. Such a decomposition has been shown to ...

The coefficients of the Markov binomial distribution are solved in terms of the underlying state-contingent probabilities of the Markov chain. This will be useful for researchers concerned with the analysis of data generated by a discrete Markov chain. The paper exploits the regenerative nature of the problem and solves the difference equations known to define the distribution. The LLN, CLT and...

Graphical Markov models use graphs ei ther undirected directed or mixed to rep resent possible dependences among statis tical variables Applications of undirected graphs UDGs include models for spatial de pendence and image analysis while acyclic directed graphs ADGs which are espe cially convenient for statistical analysis arise in such elds as genetics and psychomet rics and as models for exp...