Abstract Bayesian computations with Hidden Markov Models (HMMs) are often avoided in
practice. Instead, due to reduced running time, point estimates–maximum likelihood (ML) or …

Abstract Motivation: Hidden Markov Models (HMMs) have been widely used for biological
sequence analysis. When modeling a phenomenon where for instance the nucleotide …

Abstract Hidden Markov models (HMMs) are probabilistic models that have been extremely
successful in addressing problems in bioinformatics, error-correcting codes, natural …

In this tutorial, we discuss two main algorithms for Hidden Markov Models or HMMs: the
Viterbi algorithm and the expectation phase of the Baum–Welch algorithm, and we describe …

Hidden Markov models (HMMs) are a class of stochastic models that have proven to be
powerful tools for the analysis of molecular sequence data. A hidden Markov model can be …

We consider the problem of estimating the maximum posterior probability (MAP) state
sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the …

Abstract Hidden Markov models (HMMs) and their variants were successfully used for
several sequence annotation tasks in bioinformatics. Traditionally, inference with HMMs is …

Abstract Hidden Markov models (HMMs) are routinely used for analysis of long genomic
sequences to identify various features such as genes, CpG islands, and conserved …

Hidden Markov models (HMMs) are applied to many problems of computational Molecular
Biology. In a predictive task, the HMM is endowed with a decoding algorithm in order to …

The standard method of applying hidden Markov models to biological problems is to find a
Viterbi (maximal weight) path through the HMM graph. The Viterbi algorithm reduces the …