We propose a modified hidden Markov model (MHMM) that incorporates nonparametric state duration and state duration-dependent observation probabilities to reflect state transitions and to have accurate temporal structures in the HMM. In addition, to cope with the problem that results from the use of insufficient amount of training data, we propose to use the modified continuous density hidden Ma...

In this work the output density functions of hidden Markov models are phoneme-wise tied mixture Gaussians. For training these tied mixture density HMMs, modiied versions of the Viterbi training and LVQ based corrective tuning are described. The initialization of the mean vectors of the mixture Gaussians is performed by rst composing small Self-Organizing Maps representing each phoneme and then ...

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...

We propose a modified hidden Markov model (MHMM) thatincorporates nonparametric state duration and state duration-dependentobservation probabilities to reflect state transitions and to have accuratetemporal structures in the HMM.In addition, to cope with the problem that results from the use ofinsufficient amount of training data, we propose to use the modifiedcontinuous...

In continuous density Hidden Markov Models (HMMs) for speech recognition, the probability density function (pdf) for each state is usually expressed as a mixture of Gaussians. In this paper, we present a model in which the pdf is expressed as the convolution of two densities. We focus on the special case where one of the convolved densities is a M -Gaussian mixture, and the other is a mixture o...

background: routinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. we aimed at using a hidden markov model in order to detect the abnormal states of pulmonary tuberculosis in iran. methods: data for this study were the weekly number of newly diagnosed cases with sputum smear-positive p...

In Hidden Markov Models (HMM) the probability distribution of response Yt (∀t = 1, 2, . . . , T ) at each observation time is conditionally specified on the current hidden or latent state Xt. The sequence of hidden states follows a first order time-homogeneous Markov chain. Discrete time or continuous time HMM are respectively specified by T ⊆ N or T ⊆ R (from now on DHMM and CHMM). In this wor...

Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserved paths in these models by introducing a fast auxiliary variable Gibbs sampler. Our approach is based on the idea of uniformization, and sets up a Markov chain over paths by sampling a finite set of virtual jump times ...

Human activities are characterised by the spatio-temporal structure of their motion patterns. Such structures can be represented as temporal trajectories in a high-dimensional feature space of closely correlated measurements of visual observations. Models of such temporal structures need to account for the probabilistic and uncertain nature of motion patterns, their non-linear temporal scaling ...