Results 1 to 10 of about 41,241 (143)
Network motif detection using hidden markov models [PDF]
Scientific ReportsGraphical representations model complex networks by encoding entities as vertices and interactions as edges, with recurring subgraphs—or motifs—revealing fundamental organizational principles. We present a novel application of Hidden Markov Models (HMMs)
Costas Bampos, Vasileios Megalooikonomou
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Incorporating sparse labels into hidden Markov models using weighted likelihoods improves accuracy and interpretability in biologging studies. [PDF]
PLoS ONEEcologists often use a hidden Markov model to decode a latent process, such as a sequence of an animal's behaviours, from an observed biologging time series.
Evan Sidrow +6 more
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Validating hidden Markov models for seabird behavioural inference [PDF]
Ecology and EvolutionUnderstanding animal movement and behaviour can aid spatial planning and inform conservation management. However, it is difficult to directly observe behaviours in remote and hostile terrain such as the marine environment. Different underlying states can
Rebecca A. Akeresola +6 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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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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Visual tracking using interactive factorial hidden Markov models
IET Signal Processing, 2021 The authors present a novel tracking algorithm based on a factorial hidden Markov model (FHMM) that can utilise the structured information of a target.
Jin Wook Paeng, Junseok Kwon
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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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