Results 41 to 50 of about 63,948 (252)
Partially-Hidden Markov Models. [PDF]
, 2012 International audienceThis paper addresses the problem of Hidden Markov Models (HMM) training and inference when the training data are composed of feature vectors plus uncertain and imprecise labels.
Denoeux, Thierry +2 more
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Artificial Intelligence, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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SMCis: An Effective Algorithm for Discovery of Cis-Regulatory Modules. [PDF]
PLoS ONE, 2016 The discovery of cis-regulatory modules (CRMs) is a challenging problem in computational biology. Limited by the difficulty of using an HMM to model dependent features in transcriptional regulatory sequences (TRSs), the probabilistic modeling methods ...
Haitao Guo, Hongwei Huo, Qiang Yu
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Frontiers in Robotics and AI, 2022 In this study, HcVGH, a method that learns spatio-temporal categories by segmenting first-person-view (FPV) videos captured by mobile robots, is proposed.
Masatoshi Nagano +5 more
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Graphical models for social behavior modeling in face-to face interaction [PDF]
, 2016 International audienceThe goal of this paper is to model the coverbal behavior of a subject involved in face-to-face social interactions. For this end, we present a multimodal behavioral model based on a Dynamic Bayesian Network (DBN).
Bailly, Gérard +3 more
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Protein secondary structure prediction for a single-sequence using hidden semi-Markov models
BMC Bioinformatics, 2006 Background The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about ...
Borodovsky Mark +2 more
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Informational and Causal Architecture of Continuous-time Renewal and Hidden Semi-Markov Processes
, 2016 We introduce the minimal maximally predictive models ({\epsilon}-machines) of processes generated by certain hidden semi-Markov models. Their causal states are either hybrid discrete-continuous or continuous random variables and causal-state transitions ...
Crutchfield, James P., Marzen, Sarah E.
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Global discriminative learning for higher-accuracy computational gene prediction. [PDF]
PLoS Computational Biology, 2007 Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content.
Axel Bernal +3 more
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L-cumulants, L-cumulant embeddings and algebraic statistics
, 2012 Focusing on the discrete probabilistic setting we generalize the combinatorial definition of cumulants to L-cumulants. This generalization keeps all the desired properties of the classical cumulants like semi-invariance and vanishing for independent ...
Zwiernik, Piotr
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Introduction to Hidden Semi-Markov Models
, 2018 The purpose of this volume is to present the theory of Markov and semi-Markov processes in a discrete-time, finite-state framework. Given this background, hidden versions of these processes are introduced and related estimation and filtering results developed. The approach is similar to the earlier book, Elliott et al. (1995).
John van der Hoek, Robert J. Elliott
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