A number of software reliability models have been proposed for assessing the reliability of a software system. In this paper, we discuss the time-domain and data-domain approaches to software reliability modeling, and classify the previously reported models into these two classes based on their underlying assumptions. The data-domain models are further classiied into fault-seeding and input dom...

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDPHMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM’s strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can exten...

Many nonlinear time series models have been proposed in the last decades. Among them, the models with regime switchings provide a class of versatile and interpretable models which have received a particular attention in the literature. In this paper, we consider a large family of such models which generalize the well known Markov-switching AutoRegressive (MS-AR) by allowing non-homogeneous swit...

We consider cyclic non homogeneous fuzzy Markov chains where there are uncertainties in the transition possibilities. These uncertainties are modeled by triangular fuzzy number. Using the algorithm for finding the greatest eigen fuzzy sets we have analyzed the long term behavior of the system and this is illustrated with the numerical example. Mathematics Subject Classification: 03E72, 60J10

The prediction of non{stationary dynamical systems may be performed by identifying appropriate sub{dynamics and an early detection of mode changes. In this paper, we present a framework which uniies the mixtures of experts approach and a generalized hidden Markov model with an input{dependent transition matrix: the Hidden Markov Mixtures of Experts (HMME). The gating procedure incorporates stat...

A novel approach is developed for predicting body trajectories for cancer progression, where conditional probabilities of clinical data are modeled using Hidden Markov Model techniques. Basically, each potential body site is encoded by an N-letter code, and a disease trajectory is described in terms of a string of letters. Patient data base records are then represented by such strings with diff...

We examine the use of hidden Markov and hidden semi-Markov models for automatically segmenting an electrocardiogram waveform into its constituent waveform features. An undecimated wavelet transform is used to generate an overcomplete representation of the signal that is more appropriate for subsequent modelling. We show that the state durations implicit in a standard hidden Markov model are ill...

the air transport industry is seeking to manage risks in air travels. its main objective is to detect abnormal behaviors in various flight conditions. the current methods have some limitations and are based on studying the risks and measuring the effective parameters. these parameters do not remove the dependency of a flight process on the time and human decisions. in this paper, we used an hmm...

Both Hidden Markov Models and Neural Networks have already been used as production systems for speaker identification or verification. Recently [9] has shown that ergodic multi-state hidden Markov Models do not outperform one-state "hidden" Markov Models, i.e. Gaussian Mixture Models, for speaker recognition. She put in evidence that the important characteristic of these models is the total num...

MOTIVATION When analysing gene expression time series data, an often overlooked but crucial aspect of the model is that the regulatory network structure may change over time. Although some approaches have addressed this problem previously in the literature, many are not well suited to the sequential nature of the data. RESULTS Here, we present a method that allows us to infer regulatory netwo...