A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
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This article is published in Annals of Mathematical Statistics.The article was published on 1970-02-01 and is currently open access. It has received 4618 citations till now. The article focuses on the topics: Examples of Markov chains & Markov chain.read more
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Proceedings Article
Supervised learning for dynamical system learning
TL;DR: In this article, the authors propose a new view of dynamical system learning, which allows users to incorporate prior knowledge via standard techniques such as L1 regularization, and demonstrate the effectiveness of their framework by showing examples where nonlinear regression or lasso let us learn better state representations than plain linear regression does.
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Gait segmentation using bipedal foot pressure patterns
S.M.M. De Rossi,Simona Crea,Marco Donati,Peter Rebersek,Domen Novak,Nicola Vitiello,Tommaso Lenzi,Janez Podobnik,Marko Munih,Maria Chiara Carrozza +9 more
TL;DR: An automated gait segmentation method based on the analysis of foot plantar pressure patterns elaborated from two wireless pressure-sensitive insoles that is highly effective, yielding to an average performance of about 95% of correct phase classification, and 85 to 90% of phase transitions detected inside an acceptance window of 50ms.
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Exploitation of unlabeled sequences in hidden Markov models
Masashi Inoue,Naonori Ueda +1 more
TL;DR: By using unlabeled data, the proposed extended Baum-Welch (EBW) algorithm improves the classification performance of HMMs more robustly than the conventional naive labeling (NL) approach.
Proceedings ArticleDOI
Traffic and vehicle speed prediction with neural network and Hidden Markov model in vehicular networks
Bingnan Jiang,Yunsi Fei +1 more
TL;DR: A novel vehicle speed prediction method in the context of vehicular networks, where the real-time traffic information is accessible, and shows that the proposed method outperforms other ones in terms of prediction accuracy.
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Hidden Markov modeling using a dominant state sequence with application to speech recognition
Neri Merhav,Yariv Ephraim +1 more
TL;DR: Approximate maximum likelihood (ML) hidden Markov modeling using the most likely state sequence (MLSS) is examined and compared with the exact ML approach that considers all possible state sequences.
References
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Journal ArticleDOI
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Leonard E. Baum,Ted Petrie +1 more
Journal ArticleDOI
An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
Leonard E. Baum,J. A. Eagon +1 more
TL;DR: In this paper, a polynomial with nonnegative coefficients homogeneous of degree d in its variables is shown to be polynomially homogeneous unless 3(3(x))>P(x), where 3(x)=x.
Book ChapterDOI
The Gamma Function
Willi Freeden,Martin Gutting +1 more
TL;DR: The Gamma function as discussed by the authors is a generalized factorial function that can be used to estimate the probability distribution of a probability distribution, and it has been used in many applications, e.g., as part of probability distributions.