Asymmetric Hidden Markov Models with continuous variables

Puerto Santana, Carlos and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María (2018). Asymmetric Hidden Markov Models with continuous variables. In: "18th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2018)", 23-26 Oct 2018, Granada, España. ISBN 978-3-030-00373-9. pp. 98-107. https://doi.org/10.1007/978-3-030-00374-6_10.

Description

Title: Asymmetric Hidden Markov Models with continuous variables
Author/s:
  • Puerto Santana, Carlos
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
Item Type: Presentation at Congress or Conference (Article)
Event Title: 18th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2018)
Event Dates: 23-26 Oct 2018
Event Location: Granada, España
Title of Book: Advances in Artificial Intelligence
Date: 2018
ISBN: 978-3-030-00373-9
Subjects:
Freetext Keywords: Hidden Markov models; Bayesian networks; Model selection; Structure learning; Time series; Information asymmetries; Linear Gaussian Bayesian network
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Hidden Markov models have been successfully applied to model signals and dynamic data. However, when dealing with many variables, traditional hidden Markov models do not take into account asymmetric dependencies, leading to models with overfitting and poor problem insight. To deal with the previous problem, asymmetric hidden Markov models were recently proposed, whose emission probabilities are modified to follow a state-dependent graphical model. However, only discrete models have been developed. In this paper we introduce asymmetric hidden Markov models with continuous variables using state-dependent linear Gaussian Bayesian networks. We propose a parameter and structure learning algorithm for this new model. We run experiments with real data from bearing vibration. Since vibrational data is continuous, with the proposed model we can avoid any variable discretization step and perform learning and inference in an asymmetric information frame.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIN2016-79684-PUnspecifiedUniversidad Politécnica de MadridAvances en clasificación multidimensional y detección de anomalías con redes bayesianas
Madrid Regional GovernmentS2013/ICE-2845CASI – CAMUnspecifiedConceptos y aplicaciones de los sistemas inteligentes

More information

Item ID: 54653
DC Identifier: http://oa.upm.es/54653/
OAI Identifier: oai:oa.upm.es:54653
DOI: 10.1007/978-3-030-00374-6_10
Official URL: https://link.springer.com/content/pdf/10.1007%2F978-3-030-00374-6_10.pdf
Deposited by: Memoria Investigacion
Deposited on: 26 Apr 2019 06:59
Last Modified: 26 Apr 2019 07:39
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