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The influence of observation sequence features on the performance of the Bayesian hidden Markov model: A Monte Carlo simulation study. [PDF]
PLoS ONEThe hidden Markov model is a popular modeling strategy for describing and explaining latent process dynamics. There is a lack of information on the estimation performance of the Bayesian hidden Markov model when applied to categorical, one-level data. We
Jan-Willem Simons +2 more
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International Clinical Neuroscience Journal, 2022 Background: In this study, three detecting approaches have been proposed and evaluated for online detection of balance situations during quiet standing.
Rashin Abdolhossein Harisi +1 more
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Customer Behaviour Hidden Markov Model
Mathematics, 2022 In this work, the Customer behaviour hidden Markov model (CBHMM) is proposed to predict the behaviour of customers in e-commerce with the goal to forecast the store income. The model consists of three sub-models: Vendor, Psychology and Loyalty, returning
Ales Jandera, Tomas Skovranek
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d'CARTESIAN, 2015 Real-time processes produce observations that can be discrete, continuous, stationary, time variant, or noisy. The fundamental challenge is to characterize the observations as a parametric random process, the parameters of which should be estimated, using a well-defined approach. This allows us to construct a theoretical model of the underlying process
Mariette Awad, Rahul Khanna
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Scoring hidden Markov models [PDF]
Bioinformatics, 1997 Statistical sequence comparison techniques, such as hidden Markov models and generalized profiles, calculate the probability that a sequence was generated by a given model. Log-odds scoring is a means of evaluating this probability by comparing it to a null hypothesis, usually a simpler statistical model intended to represent the universe of sequences ...
C, Barrett, R, Hughey, K, Karplus
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Factorial Hidden Markov Models [PDF]
Machine Learning, 1997 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ghahramani, Zoubin, Jordan, Michael I.
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Utile distinction hidden Markov models [PDF]
Twenty-first international conference on Machine learning - ICML '04, 2004 This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Partially Observable Markov Decision Processes. We present a novel approach that uses a modification of the well-known Baum-Welch algorithm for learning a Hidden Markov Model (HMM)
Wierstra, D., Wiering, M.A.
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ToPS: a framework to manipulate probabilistic models of sequence data. [PDF]
PLoS Computational Biology, 2013 Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling.
André Yoshiaki Kashiwabara +5 more
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Driving style recognition method using braking characteristics based on hidden Markov model. [PDF]
PLoS ONE, 2017 Since the advantage of hidden Markov model in dealing with time series data and for the sake of identifying driving style, three driving style (aggressive, moderate and mild) are modeled reasonably through hidden Markov model based on driver braking ...
Chao Deng +3 more
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A Novel Human-Vehicle Interaction Assistive Device for Arab Drivers Using Speech Recognition
IEEE Access, 2022 About one-quarter of all car collisions in the United States are caused by distracted driving, and this ratio is expected to rise. As vehicles are equipped with more elaborate and complex technology, human-vehicle interaction via dashboard displays and ...
Ghadeer A. Jaradat +2 more
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