Drifting Markov Models with Polynomial Drift and Applications to DNA Sequences

Hidden Markov Models with Applications to DNA Sequence Analysis

The Hidden Markov Model (HMM) is a model with a finite number of states, each associated with a probability distribution. The transitions between states cannot be directly measured (hidden), but in a particular state an observation can be generated. It is the observations and not the states themselves which are visible to an outside observer. However, by applying a series of statistical techniq...

Applications of Hidden Markov Models for Characterization of Homologous DNA Sequences with a Common Gene

Identifying and characterizing the structure in genome sequences is one of the principal challenges in modern molecular biology, and comparative genomics offers a powerful tool. In this paper, we introduce a hidden Markov model that allows a comparative analysis of multiple sequences related by a phylogenetic tree, and we present an efficient method for estimating the parameters of the model. T...

On Markov and Hidden Markov Models with Applications to Trajectories

Analysis of Drifting Dynamics with Neural Network Hidden Markov Models

K. Pawelzik MPI f. Stromungsforschung Bunsenstr. 10 37073 Gottingen, Germany We present a method for the analysis of nonstationary time series with multiple operating modes. In particular, it is possible to detect and to model both a switching of the dynamics and a less abrupt, time consuming drift from one mode to another. This is achieved in two steps. First, an unsupervised training method p...

Clustering Sequences with Hidden Markov Models

This paper discusses a probabilistic model-based approach to clustering sequences, using hidden Markov models (HMMs) . The problem can be framed as a generalization of the standard mixture model approach to clustering in feature space. Two primary issues are addressed. First, a novel parameter initialization procedure is proposed, and second, the more difficult problem of determining the number...