A profile hidden Markov model (PHMM) is widely used in assigning protein sequences to protein families. In this model, the hidden states only depend on the previous hidden state and observations are independent given hidden states. In other words, in the PHMM, only the information of the left side of a hidden state is considered. However, it makes sense that considering the information of the b...

To reduce energy demand in households it is useful to know which electrical appliances are in use at what times. Monitoring individual appliances is costly and intrusive, whereas data on overall household electricity use is more easily obtained. In this paper, we consider the energy disaggregation problem where a household’s electricity consumption is disaggregated into the component appliances...

Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This article describes unsupervised classification of radar images in the framework of hidden Markov models and generalised mixture estimation. In particular, we show that hidden Markov chains, based on a Hilbert-Peano scan of the rad...

The purpose of this paper is to use the framework of hidden Markov chains for the modelling of the failure and debugging process of software, and the prediction of software reliability. The model parameters are estimated using the forward-backward EM algorithm and model selection is done with the BIC criterion. The advantages and drawbacks of this approach with respect to usual modelling are an...

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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...

This paper describes a Bayesian approach to determining the number of hidden states in a hidden Markov model (HMM) via reversible jump Markov chain Monte Carlo (MCMC) methods. Acceptance rates for these algorithms can be quite low, resulting in slow exploration of the posterior distribution. We consider a variety of reversible jump strategies which allow inferences to be made in discretely obse...

I tackle the problem of partitioning a sequence into homogeneous segments, where homogeneity is defined by a set of Markov models. The problem is to study the likelihood that a sequence is divided into a given number of segments. Here, the moments of this likelihood are computed through an efficient algorithm. Unlike methods involving Hidden Markov Models, this algorithm does not require probab...

1.1 Preliminaries In this chapter we focus on what Rabiner in his popular tutorial (Rabiner, 1989) calls “uncovering the hidden part of the model” or “Problem 2”, that is, hidden path inference. We consider a hidden Markov model (X,Y) = {(Xt,Yt)}t∈Z, where Y = {Yt}t∈Z is an unobservable, or hidden, homogeneous Markov chain with a finite state space S = {1, . . . ,K}, transition matrix P = (pi,j...