In this paper, we consider a parametric hidden Markov model where the hidden state space is non necessarily finite. We provide a necessary and sufficient condition for the invertibility of the limiting Fisher information matrix.

• The spectral properties of ergodic measure symbolic systems are investigated for applications to signal processing, pattern recognition, and anomaly detection in real-time uncertain dynamical systems. underlying algorithm is built upon the concept sequences measure-preserving transformations (MPTs) on probability spaces construction non-stationary probabilistic finite state automata (PFSA) by...

This paper presents clustering techniques that partition temporal data into homogeneous groups, and constructs state based proles for each group in the hidden Markov model (HMM) framework. We propose a Bayesian HMM clustering methodology that improves upon existing HMM clustering by incorporating HMM model size selection into clustering control structure to derive better cluster models and part...

Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Mos...

The model A hidden Markov model is characterized by a set of M states, by an initial probability distribution for the first state, by a transition probability matrix linking successive states, and by a state-dependent probability distribution on the outputs. We represent the state at time t as a multinomial random variable qt, with components q t, for i = 1, . . . ,M . Thus q t is equal to one ...

We develop a Bayesian non-parametric (BNP) model coupled with Markov random fields (MRFs) for risk mapping, to infer homogeneous spatial regions in terms of risks. In contrast most existing methods, the proposed approach does not require an arbitrary commitment specified number classes and determines their levels automatically. consider settings which relevant information are counts propose so-...

We present the first global precipitation predictability estimates corresponding to recently discovered flavors of El Niño Southern Oscillation (ENSO) that are encoded in hidden states Tropical Pacific sea surface temperatures identified using a non-homogeneous Markov model. For each calendar month and for state, we assess future through conditional standardized anomaly average standard deviati...

The Hierarchical Dirichlet Process Hidden Markov model (HDP-HMM) is a Bayesian non parametric extension of the classical Hidden Markov Model (HMM) that allows to infer posterior probability over the cardinality of the hidden space, thus avoiding the necessity of cross-validation arising in standard EM training. This paper presents the application of Hierarchical Dirichlet Process Hidden Markov ...

In the classical hidden Markov chain (HMC) model we have a hidden chain X , which is a Markov one and an observed chain Y . HMC are widely used; however, in some situations they have to be replaced by the more general “hidden semi-Markov chains” (HSMC), which are particular “triplet Markov chains” (TMC) ) , , ( Y U X T = , where the auxiliary chain U models the semi-Markovianity of X . Otherwis...

The focus of this paper is on the sensitivity to the specification of the prior in a hidden Markov model describing homogeneous segments of DNA sequences. An intron from the chimpanzee α-fetoprotein gene, which plays an important role in embryonic development in mammals is analysed. Three main aims are considered : (i) to assess the sensitivity to prior specification in Bayesian hidden Markov m...