Abstract
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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Acknowledgments
Our work was supported in part XDATA program of Defense Advanced Research Projects Agency (DARPA), administered through Air Force Research Laboratory contract FA8750-12-C-0332 and the National Institute of Health under grant number U01HL101066-01. We thank Bernardo Colombo, Guido Masarotto, Fausta Ongaro, Petra Frank-Herrmann, and other investigators of the European Study of Daily Fecundability for sharing their data with us. The empirical work described in this chapter was performed using 
, an inference engine, and GeNIe, a development environment for reasoning in graphical probabilistic models, both developed at the Decision Systems Laboratory, University of Pittsburgh, and available at http://genie.sis.pitt.edu/.
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Łupińska-Dubicka, A., Druzdzel, M.J. (2015). Modeling Dynamic Processes with Memory by Higher Order Temporal Models. In: Hommersom, A., Lucas, P. (eds) Foundations of Biomedical Knowledge Representation. Lecture Notes in Computer Science(), vol 9521. Springer, Cham. https://doi.org/10.1007/978-3-319-28007-3_14
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DOI: https://doi.org/10.1007/978-3-319-28007-3_14
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