scholarly journals Decoding algorithm for binary linear hidden Markov models represented in the form of algebraic Bayesian networks

2014 ◽  
Vol 1 (24) ◽  
pp. 165
Author(s):  
Alexander Lvovich Tulupyev ◽  
Andrey Alexandrovich Filchenkov ◽  
Anton Mikhailovich Alexeyev
Keyword(s):  
Bayesian Networks ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Decoding Algorithm

AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS

2001 ◽  
Vol 15 (01) ◽  
pp. 9-42 ◽  
Author(s):  
ZOUBIN GHAHRAMANI
Keyword(s):  
Bayesian Networks ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Recent Literature ◽  
Hidden Markov ◽  
Continuous Variables ◽  
State Variables ◽  
Exact Inference ◽  
Markov Chain Sampling ◽  
Inference Algorithms

We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective makes it possible to consider novel generalizations of hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.

scholarly journals A spark-based parallel distributed posterior decoding algorithm for big data hidden Markov models decoding problem

2021 ◽  
Vol 10 (3) ◽  
pp. 789
Author(s):  
Imad Sassi ◽  
Samir Anter ◽  
Abdelkrim Bekkhoucha
Keyword(s):  
Big Data ◽  
Hidden Markov Models ◽  
Large Scale ◽  
Markov Models ◽  
Hidden Markov ◽  
Machine Learning Algorithms ◽  
Decoding Algorithm ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Mapreduce Paradigm

<span lang="EN-US">Hidden </span><span lang="IN">M</span><span lang="EN-US">arkov models (HMMs) are one of machine learning algorithms which have been widely used and demonstrated their efficiency in many conventional applications. This paper proposes a modified posterior decoding algorithm to solve hidden Markov models decoding problem based on MapReduce paradigm and spark’s resilient distributed dataset (RDDs) concept, for large-scale data processing. The objective of this work is to improve the performances of HMM to deal with big data challenges. The proposed algorithm shows a great improvement in reducing time complexity and provides good results in terms of running time, speedup, and parallelization efficiency for a large amount of data, i.e., large states number and large sequences number.</span>

scholarly journals Representation of multinomial linear hidden Markov models in the form of algebraic Bayesian networks

2014 ◽  
Vol 1 (20) ◽  
pp. 186
Author(s):  
Leonid Markovich Revzin ◽  
Andrey Alexandrovich Filchenkov ◽  
Alexander Lvovich Tulupyev
Keyword(s):  
Bayesian Networks ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

scholarly journals Representation of binary linear hidden Markov models in the form of algebraic Bayesian networks

2014 ◽  
Vol 1 (12) ◽  
pp. 134
Author(s):  
Maria Petrovna Momzikova ◽  
Olga Igorevna Velikodnaya ◽  
Mikhail Iakovlevich Pinsky ◽  
Alexander Vladimirovich Sirotkin ◽  
Alexander Lvovich Tulupyev ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

scholarly journals An observed sequence probability estimate in binary linear hidden Markov models with posterior inference in algebraic Bayesian networks

2014 ◽  
Vol 2 (13) ◽  
pp. 122
Author(s):  
Maria Petrovna Momzikova ◽  
Olga Igorevna Velikodnaya ◽  
Mikhail Iakovlevich Pinsky ◽  
Alexander Vladimirovich Sirotkin ◽  
Alexander Lvovich Tulupyev ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Probability Estimate ◽  
Posterior Inference

Hidden Markov Models and Bayesian Networks for Counter-Terrorism

2006 ◽  
pp. 27-50 ◽  
Author(s):  
Krishna Pattipati ◽  
Peter Willett ◽  
Jeffrey Allanach ◽  
Haiying Tu ◽  
Satnam Singh
Keyword(s):  
Bayesian Networks ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Counter Terrorism
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