A Markov model, or Markov chain, can be viewed as a stochastic finite state automaton in which each transition is labeled with a probability in such a way that the set of transitions from a given state have their probability labels adding up to 1. If in addition each state is labeled with an observable symbol, the probability of a string of symbols [x1, x2, ..., xn] (abbreviated as x1,n) is the...

6.1.1 Preface: CpG islands It is known that due to biochemical considerations that CpG, the pair of nocleotides C and G, appearing successively, in this order, along one DNA starnd, is relatively rare in DNA sequences, excluding particular sub-sequences, which are several hundreds of nucleotides long, where the couple CpG is more frequent. These sub-sequences, called CpG islands, are known to a...

The technique of hidden Markov models has been established as one of the most successful methods applied to the problem of speech recognition. However, its performance is considerably degraded when the speech signal is contaminated by noise. This work presents a technique which improves the performance of hidden Markov models when these models are used in different noise conditions during the s...

We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization of HMMs that can model smooth state evolution as in Linear Dynamical Systems (LDSs) as well as non-log-concave predictive distributions as in continuous-observation HMMs. RR-HMMs assume anm-dimensional latent state and n discrete observations, with a transition matrix of rank k ≤ m. This implies the dynamics evolve in ...

Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks. We then consider the major bioinformatics application...

We aim at tackling the problem of inadequate specification a Markov manpower model in this paper, by formulating procedure for validating inclusion or non-inclusion some transition parameters model. The mover-stayer principle and its extensions are employed to incorporate hidden classes achieve more homogeneity is compared with without classes, which parsimonious, using Likelihood ratio statist...

Hidden Markov models (HMM) are well known in speech recognition, where they are trained to recognize spoken words and even whole sentences. They are used to find the parameters of a so-called hidden model (usually a DTMC) by training it with observed output sequences. This paper introduces an approach to train stochastic Petri nets with the methods of HMM. As opposed to a DTMC, a stochastic Pet...

We introduce a probabilistic model called a Situated State Hidden Markov Model (SSHMM), in which states arèsituated' (i.e. assigned positions) and assumed to correspond to regions of an underlying continuous state space. Transition probabilities among states are induced by the assigned state positions in such a way that transitions occur more frequently between nearby states. The model is forma...

Carl D. Mitchell 1 Mary P. Harper 2 Leah H. Jamieson 2 1AT&T Bell Laboratories, 600 Mountain Ave., Murray Hill, NJ 07974, [email protected] 2School of Electrical and Computer Engineering, Purdue University West Lafayette, IN 47907-1285, flhj,[email protected] ABSTRACT Hybrids that use a neural network to estimate the output probabilitiy for a hidden Markov model (HMM) word recognizer ha...

In a clinical trial of a treatment for alcoholism, a common response variable of interest is the number of alcoholic drinks consumed by each subject each day, or an ordinal version of this response, with levels corresponding to abstinence, light drinking, and heavy drinking. In these trials, within-subject drinking patterns are often characterized by alternating periods of heavy drinking and ab...