We compare a hidden Markov and Erlang semi-Markov modeling of a partially observable deteriorating system operating under a varying load and subject to multi-sensor vibration monitoring. The evolution of the unknown state process is described by a hidden, two state semi-Markov process with an Erlang sojourn time distribution in the healthy state. The unknown model parameters are estimated using...

Hidden Markov models play a critical role in the modelling and problem solving of important AI tasks such as speech recognition and natural language processing. However, the students often have difficulty in understanding the essence and applications of Hidden Markov models in the context of a cursory introductory coverage of the subject. In this paper, we describe an empirical approach to expl...

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 viewed as a black box that generates sequences of observations. The unobservable internal state of the box is stochastic and is determined by a finite state Markov chain. The observable output is stochastic with distribution...

background : to prepare safe and adequate blood supply to meet patients’ needs and ensure a sufficient number of regular blood donors, knowledge about factors encouraging people to donate blood regularly is essential. considering its importance, we aimed to identifying the effective factors of the return to blood donation in based on zero-inflated count regression models using bayesian approach...

in this paper we address the issue of recognizing farsi handwritten words. two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. these are directional and intensity gradient features. the feature vector extracted from each stripe is then coded using the self organizing map (som). in this method each word is modeled using the discrete hidde...

mobile ad-hoc networks have attracted a great deal of attentions over the past few years. considering their applications, the security issue has a great significance in them. security scheme utilization that includes prevention and detection has the worth of consideration. in this paper, a method is presented that includes a multi-level security scheme to identify intrusion by sensors and authe...

We discuss strong Markov property of Poisson point processes and the related stopping sets. Viewing Poisson process as a set indexed random field, we demonstrate how the martingale technique applies to establish the analogues of the classical results: Doob’s theorem, Wald identity in this multi-dimensional setting. In particular, we show that the famous Slivnyak-Mecke theorem characterising the...

— The aim of the present paper is to document the need for adapting the definition of hidden Markov models (HMM) to population studies, which rigorous interpretation typically requires the use of mixed-effects models, as well as for corresponding learning methodologies. In this article, mixed hidden Markov models (MHMM) are introduced through a brief state of the art on hidden Markov models and...

Two distinct strands of research are developed: new methodology for inference on the Markov modulated Poisson process (MMPP), and new theory on optimal scaling for the random walk Metropolis (RWM). A novel technique is presented for simulating from the exact distribution of a continuous time Markov chain over an interval given the start and end states and the infinitesimal generator. This is us...

Among the class of discrete time Markovian processes, two models are widely used, the Markov chain and the Hidden Markov Model. A major di erence between these two models lies in the relation between successive outputs of the observed variable. In a visible Markov chain, these are directly correlated while in hidden models they are not. However, in some situations it is possible to observe both...