Results 11 to 20 of about 203,293 (263)
Hidden Markov models: Pitfalls and opportunities in ecology
Methods in Ecology and Evolution, 2023 Hidden Markov models (HMMs) and their extensions are attractive methods for analysing ecological data where noisy, multivariate measurements are made of a hidden, ecological process, and where this hidden process is represented by a sequence of discrete ...
Richard Glennie +5 more
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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
doaj +1 more source
Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models
IEEE Access, 2016 In this paper, a joint feature selection and parameter estimation algorithm is presented for hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs).
Stephen Adams +2 more
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Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions [PDF]
, 2012 We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in ...
Langrock, R. +5 more
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Frontiers in Ecology and Evolution, 2021 The study of animal behavioral states inferred through hidden Markov models and similar state switching models has seen a significant increase in popularity in recent years.
Giada Sacchi, Ben Swallow
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S-estimation of hidden Markov models [PDF]
, 2014 A method for robust estimation of dynamic mixtures of multivariate distributions is proposed. The EM algorithm is modified by replacing the classical M-step with high breakdown S-estimation of location and scatter, performed by using the bisquare ...
Farcomeni, Alessio, L., Greco
core +1 more source
Journal of Artificial Intelligence Research, 2006 Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters. This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evaluation, most likely hidden state sequence and ...
Kersting, K., De Raedt, Luc, Raiko, T.
openaire +3 more sources
SOLUTION TO EVALUATION PROBLEM OF HIDDEN SEMI-MARKOV QP-MODELS
Advanced Engineering Research, 2014 A hidden semi-Markov QP-model is considered; and the way it could be embedded in a general hidden semi-Markov model is shown. The estimation problem (the first of three classical theory problems of the hidden Markov models and hidden semi-Markov models ...
V. M. Deundyak, M. A. Zhdanova
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Limit Theorems in Hidden Markov Models [PDF]
, 2012 In this paper, under mild assumptions, we derive a law of large numbers, a central limit theorem with an error estimate, an almost sure invariance principle and a variant of Chernoff bound in finite-state hidden Markov models. These limit theorems are of
Han, Guangyue
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Second-Order Belief Hidden Markov Models [PDF]
, 2014 Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model.
A. Aregui +17 more
core +5 more sources