scholarly journals Incorporating Telemetry Error into Hidden Markov Models of Animal Movement Using Multiple Imputation

2017 ◽  
Vol 22 (3) ◽  
pp. 249-269 ◽  
Author(s):  
Brett T. McClintock
Keyword(s):  
Multiple Imputation ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Animal Movement ◽  
Hidden Markov

scholarly journals Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models

2019 ◽  
Vol 10 (9) ◽  
pp. 1536-1550 ◽  
Author(s):  
Timo Adam ◽  
Christopher A. Griffiths ◽  
Vianey Leos‐Barajas ◽  
Emily N. Meese ◽  
Christopher G. Lowe ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Animal Movement ◽  
Hidden Markov ◽  
Joint Modelling ◽  
Multi Scale ◽  
Movement Data ◽  
Hierarchical Hidden Markov Models

scholarly journals Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures

2017 ◽  
Vol 22 (3) ◽  
pp. 232-248 ◽  
Author(s):  
Vianey Leos-Barajas ◽  
Eric J. Gangloff ◽  
Timo Adam ◽  
Roland Langrock ◽  
Floris M. van Beest ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Animal Movement ◽  
Hidden Markov ◽  
Hierarchical Structures ◽  
Multi Scale ◽  
General Behavior ◽  
Scale Modeling ◽  
Multi Scale Modeling

scholarly journals momentuHMM: R package for generalized hidden Markov models of animal movement

2018 ◽  
Vol 9 (6) ◽  
pp. 1518-1530 ◽  
Author(s):  
Brett T. McClintock ◽  
Théo Michelot
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Animal Movement ◽  
Hidden Markov ◽  
R Package

Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement

2017 ◽  
Vol 22 (3) ◽  
pp. 270-293 ◽  
Author(s):  
Jennifer Pohle ◽  
Roland Langrock ◽  
Floris M. van Beest ◽  
Niels Martin Schmidt
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Animal Movement ◽  
Hidden Markov

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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