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Modeling dynamic processes with memory by higher order temporal models

Łupińska-Dubicka, A and Druzdzel, MJ (2015) Modeling dynamic processes with memory by higher order temporal models. In: UNSPECIFIED UNSPECIFIED, 219 - 232. ISBN UNSPECIFIED

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Most practical uses of Dynamic Bayesian Networks (DBNs) involve temporal influences of the first order, i.e., influences between neighboring time steps. This choice is a convenient approximation influenced by the existence of efficient algorithms for first order models and limitations of available tools. In this paper, we focus on the question whether constructing higher time-order models is worth the effort when the underlying system’s memory goes beyond the current state. We present the results of an experiment in which we successively introduce higher order DBN models monitoring woman’s monthly cycle and measure the accuracy of these models in estimating the fertile period around the day of ovulation. We show that higher order models are more accurate than first order models. However, we have also observed over-fitting and a resulting decrease in accuracy when the time order chosen is too high.


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Item Type: Book Section
Status: Published
CreatorsEmailPitt UsernameORCID
Łupińska-Dubicka, A
Druzdzel, MJmarek@sis.pitt.eduDRUZDZEL0000-0002-7598-2286
Date: 1 January 2015
Date Type: Publication
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 9521 L
Page Range: 219 - 232
DOI or Unique Handle: 10.1007/978-3-319-28007-3_14
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 0302-9743
Date Deposited: 19 Jul 2016 12:57
Last Modified: 26 Dec 2021 13:55


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