Hybrid time Bayesian networks
Autor: | Peter J. F. Lucas, Maarten van der Heijden, Manxia Liu, Arjen Hommersom |
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Přispěvatelé: | Department Computer Science, RS-Research Line Resilience (part of LIRS program) |
Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
Computer science
02 engineering and technology Machine learning computer.software_genre Dynamic systems 01 natural sciences Theoretical Computer Science 010104 statistics & probability Artificial Intelligence Software Science 0202 electrical engineering electronic engineering information engineering Graphical model 0101 mathematics Dynamic Bayesian network Continuous time Bayesian networks business.industry Applied Mathematics Probabilistic logic Bayesian network Statistical model Variable-order Bayesian network Dynamic Bayesian networks Bayesian statistics 020201 artificial intelligence & image processing Bayesian programming Artificial intelligence business computer Software |
Zdroj: | International Journal of Approximate Reasoning, 80, 460-474. Elsevier Science Inc. Liu, M, Hommersom, A, van der Heijden, M & Lucas, P J F 2017, ' Hybrid time Bayesian networks ', International Journal of Approximate Reasoning, vol. 80, pp. 460-474 . https://doi.org/10.1016/j.ijar.2016.02.009 International Journal of Approximate Reasoning, 80, 460-474 International Journal of Approximate Reasoning, 80, pp. 460-474 |
ISSN: | 0888-613X |
DOI: | 10.1016/j.ijar.2016.02.009 |
Popis: | Capturing heterogeneous dynamic systems in a probabilistic model is a challenging problem. A single time granularity, such as employed by dynamic Bayesian networks, provides insufficient flexibility to capture the dynamics of many real-world processes. The alternative is to assume that time is continuous, giving rise to continuous time Bayesian networks. Here the problem is that the level of temporal detail is too precise to match available probabilistic knowledge. In this paper, we present a novel class of models, called hybrid time Bayesian networks, which combine discrete-time and continuous-time Bayesian networks. The new formalism allows us to more naturally model dynamic systems with regular and irregularly changing variables. We also present a mechanism to construct discrete-time versions of hybrid models and an EM-based algorithm to learn the parameters of the resulting BNs. Its usefulness is illustrated by means of a real-world medical problem. Hybrid time Bayesian networks are defined.This new class of models allows reasoning with random variables that evolve regularly or irregularly.A discrete-time characterization of these new models is given.As an application of hybrid time Bayesian networks a medical example is modeled. |
Databáze: | OpenAIRE |
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