scholarly journals Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Nicola J. Quick ◽  
Saana Isojunno ◽  
Dina Sadykova ◽  
Matthew Bowers ◽  
Douglas P. Nowacek ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Diving Behaviour ◽  
Pilot Whales

scholarly journals Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence

2018 ◽  
Author(s):  
Manh Cuong Ngôe ◽  
Mads Peter Heide-Jørgensen ◽  
Susanne Ditlevsen
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Well Being ◽  
Diving Behaviour ◽  
The Arctic ◽  
Data Set ◽  
East Greenland ◽  
Species Response ◽  
Surface Time

AbstractDiving behaviour of narwhals is still largely unknown. We build three-state Hidden Markov models (HMM) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenland over 83 days, an extraordinarily long and rich data set. In particular, diurnal patterns in diving behaviour for a marine mammal is being inferred, by using periodic B-splines with boundary knots in 0 and 24 hours. Several HMMs with covariates are used to characterize dive patterns. Narwhal diving patterns have not been analysed like this before, but in studies of other whale species, response variables have been assumed independent. We extend the existing models to allow for dependence between state distributions, and show that the dependence has an impact on the conclusions drawn about the diving behaviour. It is thus paramount to relax this strong and biologically unrealistic assumption to obtain trustworthy inferences.Author summaryNarwhals live in pristine environments. However, the increase in average temperatures in the Arctic and the concomitant loss of summer sea ice, as well as increased human activities, such as ship traffic and mineral exploration leading to increased noise pollution, are changing the environment, and therefore probably also the behavior and well-being of the narwhal. Here, we use probabilistic models to unravel the diving and feeding behavior of a male narwhal, tagged in East Greenland in 2013, and followed for nearly two months. The goal is to gain knowledge of the whales’ normal behavior, to be able to later detect possible changes in behavior due to climatic changes and human influences. We find that the narwhal uses around two thirds of its time searching for food, it typically feeds during deep dives (more than 350 m), and it can have extended periods, up to 3 days, without feeding activity.

scholarly journals Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence

2019 ◽  
Vol 15 (3) ◽  
pp. e1006425 ◽  
Author(s):  
Manh Cuong Ngô ◽  
Mads Peter Heide-Jørgensen ◽  
Susanne Ditlevsen
Keyword(s):  
Hidden Markov Models ◽  
Long Range ◽  
Markov Models ◽  
Hidden Markov ◽  
Diving Behaviour ◽  
Long Range Dependence ◽  
Dependent State

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.

Market Regime Identification Using Hidden Markov Models

2016 ◽  
Author(s):  
Yuan Yuan ◽  
Gautam Mitra
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Market Regime
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