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Fuzzy Hidden Markov Chain Based Models for Time-Series Data
The hidden Markov model (HMM) has shown a remarkable capability when dealing with time series data. However, when extended to multiple sequence...
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Approximate Bayesian Estimation of Stochastic Volatility in Mean Models Using Hidden Markov Models: Empirical Evidence from Emerging and Developed Markets
The stochastic volatility in mean (SVM) model proposed by Koopman and Uspensky (J Appl Econ 17:667–689, 2002) is revisited. This paper has two goals....
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Evaluation of vicinity-based hidden Markov models for genotype imputation
BackgroundThe decreasing cost of DNA sequencing has led to a great increase in our knowledge about genetic variation. While population-scale projects...
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Functional concurrent hidden Markov model
This study considers a functional concurrent hidden Markov model. The proposed model consists of two components. One is a transition model for...
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Hidden Markov Models and Applications
This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents...
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Comparing maximum likelihood and Bayesian methods for fitting hidden Markov models to multi-state capture-recapture data of invasive carp in the Illinois River
BackgroundHidden Markov Models (HMMs) are often used to model multi-state capture-recapture data in ecology. However, a variety of HMM modeling...
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Hidden Markov model with missing emissions
In a Hidden Markov model (HMM), from hidden states, the model generates emissions that are visible. Generally, the problems to be solved by such...
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Stress testing for IInd pillar life-cycle pension funds using hidden Markov model
This paper presents a stress testing technique based on a hidden Markov regime switching model and scenario generations. Firstly, we assume that...
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Jobs-housing balance and travel patterns among different occupations as revealed by Hidden Markov mixture models: the case of Hong Kong
The spatial mismatch between jobs and housing in cities creates long daily travels that exacerbate climate change, air pollution, and traffic...
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Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models
Greater eyes-focused eye movement pattern during face recognition is associated with better performance in adults but not in children. We test the...
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Pairwise Markov Models and Hybrid Segmentation Approach
The article studies segmentation problem (also known as classification problem) with pairwise Markov models (PMMs). A PMM is a process where the...
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A secure adaptive Hidden Markov Model-based JPEG steganography method
This study introduces J-HMMSteg , an adaptive and secure JPEG image steganography technique designed for data embedding with minimal distortion....
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Partially Hidden Markov Chain Multivariate Linear Autoregressive model: inference and forecasting—application to machine health prognostics
Time series subject to regime shifts have attracted much interest in domains such as econometry, finance or meteorology. For discrete-valued regimes,...
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Hidden Markov Models of Evidence Accumulation in Speeded Decision Tasks
Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and...
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Applying Hidden Markov Modelling to Fine-Scale Telemetry
Recent developments in fine-scale acoustic telemetry have resulted in large datasets containing highly detailed information on fish movement. A...
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Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach
Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models...
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A Hidden Markov Model-based fuzzy modeling of multivariate time series
This study elaborates on a novel Hidden Markov Model (HMM)-based fuzzy model for time series prediction. Fuzzy rules (rule-based models) are employed...
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Prediction of schizophrenia from activity data using hidden Markov model parameters
In this paper, we address the problem of predicting schizophrenia based on a persons measured motor activity over time. A key challenge to achieve...
