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dc.contributor.authorCobb, Barry R.
dc.contributor.authorShenoy, Prakash P.
dc.date.accessioned2018-02-02T22:31:56Z
dc.date.available2018-02-02T22:31:56Z
dc.date.issued2017-03-17
dc.identifier.citationCobb, B. R. and Shenoy, P. P. (2017), Inference in Hybrid Bayesian Networks with Nonlinear Deterministic Conditionals. Int. J. Intell. Syst., 32: 1217–1246. doi:10.1002/int.21897en_US
dc.identifier.urihttp://hdl.handle.net/1808/25911
dc.descriptionThis is the peer reviewed version of the following article: Cobb, B. R. and Shenoy, P. P. (2017), Inference in Hybrid Bayesian Networks with Nonlinear Deterministic Conditionals. Int. J. Intell. Syst., 32: 1217–1246. doi:10.1002/int.21897, which has been published in final form at https://doi.org/10.1002/int.21897. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.en_US
dc.description.abstractTo enable inference in hybrid Bayesian networks (BNs) containing nonlinear deterministic conditional distributions, Cobb and Shenoy in 2005 propose approximating nonlinear deterministic functions by piecewise linear (PL) ones. In this paper, we describe a method for finding PL approximations of nonlinear functions based on a penalized mean square error (MSE) heuristic, which consists of minimizing a penalized MSE function subject to two principles, domain and symmetry. We illustrate our method for some commonly used one-dimensional and two-dimensional nonlinear deterministic functions such as math formula, math formula, math formula, and math formula. Finally, we solve two small examples of hybrid BNs containing nonlinear deterministic conditionals that arise in practice.en_US
dc.publisherWileyen_US
dc.titleInference in Hybrid Bayesian Networks with Nonlinear Deterministic Conditionals.en_US
dc.typeArticleen_US
kusw.kuauthorShenoy, Prakash P.
kusw.kudepartmentBusinessen_US
dc.identifier.doi10.1002/int.21897en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-8425-896Xen_US
kusw.oaversionScholarly/refereed, author accepted manuscripten_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.rights.accessrightsopenAccessen_US


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