Results 61 to 70 of about 116,234 (262)
Complexity, 2018 We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography.
Nebojša Malešević +5 more
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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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A Spectral Algorithm for Inference in Hidden Semi-Markov Models
Journal of Machine Learning Research, 2014 Hidden semi-Markov models (HSMMs) are latent variable models which allow latent state persistence and can be viewed as a generalization of the popular hidden Markov models (HMMs). In this paper, we introduce a novel spectral algorithm to perform inference in HSMMs.
MelnykIgor, BanerjeeArindam
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Peer Community JournalState-space models are widely used in ecology to infer hidden behaviors. This study develops an extensive numerical simulation-estimation experiment to evaluate the state decoding accuracy of four simple state-space models.
Bez, Nicolas +8 more
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Frontiers in Robotics and AI, 2019 Humans perceive continuous high-dimensional information by dividing it into meaningful segments, such as words and units of motion. We believe that such unsupervised segmentation is also important for robots to learn topics such as language and motion ...
Masatoshi Nagano +6 more
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IEEE Access, 2023 There is ongoing interest in the dynamics of resting state brain networks (RSNs) as potential predictors of cognitive and behavioural states. Multivariate Autoregressors (MAR) are used to model regional brain activity as a linear combination of past ...
Hernan Hernandez Larzabal +6 more
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Sequential Bayesian Learning for Hidden Semi-Markov Models
, 2023 In this paper, we explore the class of the Hidden Semi-Markov Model (HSMM), a flexible extension of the popular Hidden Markov Model (HMM) that allows the underlying stochastic process to be a semi-Markov chain. HSMMs are typically used less frequently than their basic HMM counterpart due to the increased computational challenges when evaluating the ...
Aschermayr, Patrick +1 more
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First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data
IEEE Access, 2020 Failure prognostics aims at predicting the object equipment's future degradation trend and derives the remaining useful life with a predefined failure threshold.
Jie Liu
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Consistency of maximum likelihood estimation for some dynamical systems [PDF]
, 2014 We consider the asymptotic consistency of maximum likelihood parameter estimation for dynamical systems observed with noise. Under suitable conditions on the dynamical systems and the observations, we show that maximum likelihood parameter estimation is ...
McGoff, Kevin +3 more
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Infinite Structured Hidden Semi-Markov Models
, 2014 23 pages, 10 ...
Huggins, Jonathan H., Wood, Frank
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