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Modeling Markov sources and hidden Markov models by P systems
In this work, we provide several algorithms to obtain stochastic transition P systems from Markov sources and Hidden Markov Models. In both cases,...
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Hidden Markov Models
A Markov model describes a sequence of possible states with random transition between states where the probability of the current state depends only...
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Hamptonese and Hidden Markov Models
James Hampton was a quiet and secretive man who left behind a monumental work of visionary art, along with a strange handwritten script. Hampton's...
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Expectile hidden Markov regression models for analyzing cryptocurrency returns
In this paper we develop a linear expectile hidden Markov model for the analysis of cryptocurrency time series in a risk management framework. The...
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Finding the number of latent states in hidden Markov models using information criteria
Hidden Markov models (HMMs) are often used to model time series data and are applied in many fields of research. However, estimating the unknown...
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Reversed particle filtering for hidden markov models
We present an approach to selecting the distributions in sampling-resampling which improves the efficiency of the weighted bootstrap. To complement...
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An expectation maximization algorithm for the hidden markov models with multiparameter student-t observations
Hidden Markov models are a class of probabilistic graphical models used to describe the evolution of a sequence of unknown variables from a set of...
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Matrix-Variate Hidden Markov Regression Models: Fixed and Random Covariates
Two families of matrix-variate hidden Markov regression models (MV-HMRMs) are here introduced. The distinction between them relies on the role of the...
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Estimation and Strong Approximation of Hidden Markov Models
We give novel conditions for the existence of the limit of the normalized log-likelihood function for a finite-state continuous read-out Hidden...
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Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data
We propose a variable selection method for multivariate hidden Markov models with continuous responses that are partially or completely missing at a...
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Hidden Markov Models
This chapter introduces hidden Markov models (HMMs), which can be viewed as an extension of mixture models, in which a unit of observation (e.g., a...
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Large-scale dependent multiple testing via hidden semi-Markov models
Large-scale multiple testing is common in the statistical analysis of high-dimensional data. Conventional multiple testing procedures usually...
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Hidden Markov models for longitudinal rating data with dynamic response styles
This work deals with the analysis of longitudinal ordinal responses. The novelty of the proposed approach is in modeling simultaneously the temporal...
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Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed)
BackgroundGenomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying...
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Machine Learning-Based Attack Detection for Wireless Sensor Network Security Using Hidden Markov Models
The progress of Wireless Sensor Networks (WSNs) technologies has introduced a greater susceptibility of sensors and networks to being victims of...
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A Latent Hidden Markov Model for Process Data
Response process data from computer-based problem-solving items describe respondents’ problem-solving processes as sequences of actions. Such data...
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On robust estimation of hidden semi-Markov regime-switching models
Regime-switching models provide an efficient framework for capturing the dynamic behavior of data observed over time and are widely used in economic...
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Hidden Markov Models with Unobservable Transitions
We consider Hidden Markov Models (HMMs) that admit unobservable \(\varepsilon \)...
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Inhomogeneous hidden semi-Markov models for incompletely observed point processes
A general class of inhomogeneous hidden semi-Markov models (IHSMMs) is proposed for modelling partially observed processes that do not necessarily...
