How can we apply machine learning to data that is represented as a sequence of observations over time? For instance, we might be interested in discovering the sequence of words that someone spoke based on an audio recording of their speech. Or we might be interested in annotating a sequence of words with their part-of-speech tags. These notes provides a thorough mathematical introduction to the...

An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of Baum and Petrie on finite-state finite-alphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a gener...

stemming is the process of finding the main morpheme of a word andit is used in natural language processing, text mining and informationretrieval systems. a stemmer extracts the stem of the words. we can classifypersian stemmers in to three main classes: structural stemmers, dictionarybased stemmers and  statistical stemmers.the precision of structural stemmers is low and the expenses of dictio...

The restoration of a hidden process X from an observed process Y is often performed in the framework of hidden Markov chains (HMC). HMC have been recently generalized to triplet Markov chains (TMC). In the TMC model one introduces a third random chain U and assumes that the triplet T = (X,U, Y ) is a Markov chain (MC). TMC generalize HMC but still enable the development of efficient Bayesian al...

Parallel recordings of spike trains of several single cortical neurons in behaving monkeys were analyzed as a hidden Markov process. The parallel spike trains were considered as a multivariate Poisson process whose vector firing rates change with time. As a consequence of this approach, the complete recording can be segmented into a sequence of a few statistically discriminated hidden states, w...

gesture and motion recognition are needed for a variety of applications. the use of human hand motions as a natural interface tool has motivated researchers to conduct research in the modeling, analysis and recognition of various hand movements. in particular, human-computer intelligent interaction has been a focus of research in vision-based gesture recognition. in this work, we introduce a 3-...

an electric arc furnace (eaf) is known as nonlinear and time variant load that causes power quality (pq) problems such as, current, voltage and current harmonics, voltage flicker, frequency changes in power system. one of the most important problems to study the eaf behavior is the choice of a suitable model for this load. hence, in this paper, a probabilistic three-phase model is proposed base...

nowadays, hidden markov models are extensively utilized for modeling stochastic processes. these models help researchers establish and implement the desired theoretical foundations using markov algorithms such as forward one. however, using stability hypothesis and the mean statistic for determining the values of markov functions on unstable statistical data set has led to a significant reducti...

It was recently pointed out that identifiability of quantum random walks and hidden Markov processes underlie the same principles. This analogy immediately raises questions on the existence of hidden states also in quantum random walks and their relationship with earlier debates on hidden states in quantum mechanics. The overarching insight was that not only hidden Markov processes, but also qu...