Missing data are often a problem in statistical modeling. The Bayesian paradigm offers a natural modelbased solution for this problem by treating missing values as random variables and estimating their posterior distributions. This paper reviews the Bayesian approach and describes how the MCMC procedure implements it. Beginning with SAS/STAT® 12.1, PROC MCMC automatically samples all missing va...

The Baum-Welch (EM) algorithm is a familiar tool for calculation the maximum likelihood estimate of the parameters in hidden Markov chain models. For the particular case of a binary Markov chain corrupted by binary channel noise a detailed study is carried out of the innuence that the initial conditions impose on the results produced by the algorithm.

We consider an extension of Discrete Time Markov Chain queueing model to the quantum domain by use of Discrete Time Quantum Markov Chain. We introduce methods for numerical analysis of such models. Using this tools we show that quantum model behaves fundamentally differently from the classical one.

This paper mainly analyzes the applications of the Generator matrices in a Continuous Time Markov Chain (CTMC). Hidden Markov models [HMMs] together with related probabilistic models such as Stochastic Context-Free Grammars [SCFGs] are the basis of many algorithms for the analysis of biological sequences. Combined with the continuous-time Markov chain theory of likelihood based phylogeny, stoch...

The role of probabilistic models in the debate over genetics and insurance is discussed. A Markov model is used to show that, under quite extreme assumptions, adverse selection in life insurance ought to be controllable. The statistical problems of estimating small differences in mortality are discussed; these might limit the use of many genetic disorders as rating factors. The influence of the...

Hidden Markov Models (HMM) are probabilistic graphical models for interdependent classification. In this paper we experiment with different ways of combining the components of an HMM for document analysis applications, in particular for finding tables in text. We show: a) how to integrate different document structure finders into the HMM; b) that transition probabilities should vary along the c...

We outline the use of a data-free inference procedure for estimation of uncertain model parameters for a chemical model of methane-air ignition. The method involves a nested pair of Markov chains, exploring both the data and parametric spaces, to discover a pooled joint posterior consistent with available information. We describe the highlights of the method, and detail its particular implement...

This paper proposes markovian models in portfolio theory and risk management. At first, we describe discrete time optimal allocation models. Then, we examine the investor’s optimal choices either when the returns are uniquely determined by their mean and variance or when they are modeled by a Markov chain. We subject these models to back-testing on out-of-sample data, in order to assess their f...

In order to study genetic algorithms in co-evolutionary environments, we construct a Markov model of co-evolution of populations with fixed, finite population sizes. In this combined Markov model, the behavior toward the limit can be utilized to study the relative performance of the algorithms. As an application of the model, we perform an analysis of the relative performance of haploid versus ...