In this paper we propose hidden Markov models to model electropherograms from DNA sequencing equipment and perform basecalling. We model the state emission densities using artificial neural networks, and modify the Baum–Welch reestimation procedure to perform training. Moreover, we develop a method that exploits consensus sequences to label training data, thus minimizing the need for hand label...

A. Definition A hidden Markov model is a tool for representing probability distributions over sequences of observations [1]. In this model, an observation Xt at time t is produced by a stochastic process, but the state Zt of this process cannot be directly observed, i.e. it is hidden [2]. This hidden process is assumed to satisfy the Markov property, where state Zt at time t depends only on the...

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 ...

In this paper, we study large deviations of maximum likelihood and related estimators for hidden Markov models. A hidden Markov model consists of parameterized Markov chains in a Markovian random environment, with the underlying environmental Markov chain viewed as missing data. A difficulty with parameter estimation in this model is the non-additivity of the log-likelihood function. Based on a...

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...

The following chapter can be understood as one sort of brief introduction to the history and basics of the Hidden Markov Models. Hidden Markov Models (HMMs) are learnable finite stochastic automates. Nowadays, they are considered as a specific form of dynamic Bayesian networks. Dynamic Bayesian networks are based on the theory of Bayes (Bayes & Price, 1763). A Hidden Markov Model consists of tw...

The objective of this tutorial is to introduce basic concepts of a Hidden Markov Model (HMM) as a fusion of more simple models such as a Markov chain and a Gaussian mixture model. The tutorial is intended for the practicing engineer, biologist, linguist or programmer who would like to learn more about the above mentioned fascinating mathematical models and include them into one’s repertoire. Th...

In this paper, we model operator states using hidden Markov models applied to human supervisory control behaviors. More specifically, we model the behavior of an operator of multiple heterogeneous unmanned vehicle systems. The hidden Markov model framework allows the inference of higher operator states from observable operator interaction with a computer interface. For example, a sequence of op...

Hidden Markov Model theory is an extension of the Markov Model process. It has found uses in such areas as speech recognition, target tracking and word recognition. One area which has received little in the way of research interest, is the use of Hidden Markov Models in character recognition. In this paper the application of Hidden Markov Model theory to dynamic character recognition is investi...