Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks Cano Utrera, Andrés Gómez Olmedo, Manuel Moral Callejón, Serafín Abellán Mulero, Joaquín Credal network Probability intervals Bayesian networks Strong independence Hill-climbing Branch-and-bound algorithms Inteligencia artificial Artificial intelligence This paper proposes two new algorithms for inference in credal networks. These algorithms enable probability intervals to be obtained for the states of a given query variable. The first algorithm is approximate and uses the hill-climbing technique in the Shenoy–Shafer architecture to propagate in join trees; the second is exact and is a modification of Rocha and Cozman’s branch-and-bound algorithm, but applied to general directed acyclic graphs. 2022-11-11T07:54:35Z 2022-11-11T07:54:35Z 2006-09-29 info:eu-repo/semantics/conferenceObject Andrés Cano... [et al.]. Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks, International Journal of Approximate Reasoning, Volume 44, Issue 3, 2007, Pages 261-280, ISSN 0888-613X, [https://doi.org/10.1016/j.ijar.2006.07.020] https://hdl.handle.net/10481/77902 10.1016/j.ijar.2006.07.020 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier