PROJECTS ON INTRODUCTION AND INFERENCE Course 2007/2008 Concha Bielza, Pedro Larrañaga Universidad Politécnica de Madrid.

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PROJECTS ON INTRODUCTION AND INFERENCE Course 2007/2008 Concha Bielza, Pedro Larrañaga Universidad Politécnica de Madrid

Possible projects 1 Canonical models for the CPTs: Noisy OR modelisations 2 Context-specific independence: X and Y are c.i. given Z in context C=c if P(X|Y,Z,C=c)=P(X|Z,C=c) S.Srinivas (1993) A generalization of the noisy OR model. UAI-93 F.J.Díez (1993) Parameter adjustment in Bayes networks. The generalized noisy-OR gate. UAI in Neapolitan’s book C.Boutilier, N.Friedman, M.Goldszmidt, D.Koller (1996) Context-specific independence in Bayesian networks. UAI-96, Acceptable project topics can include literature reviews, expert systems, theoretical work, computer software. If you need help selecting a project, be sure to ask us for ideas. We encourage you to submit projects that are relevant to your dissertation. Some possibilities about the part covering Basics+Inference:

Possible projects 3 Modeling tricks: parent divorcing, time-stamped models, expert disagreements, interventions… 2.3 in Jensen’s book 4 Abductive inference J.A.Gámez (2004) Abductive inference in Bayesian networks: A review. In Gámez, J.A., Moral, S., Salmerón, A., eds.: Advances in Bayesian Networks, Springer, Importance sampling for approximate inference L.Hernández, S.Moral, A.Salmerón (1998) A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques, Int. J. of Approx. Reasoning 18, C.Yuan, M.Druzdzel (2005) Importance sampling algorithms for Bayesian networks: Principles and performance, Mathematical and Computer Modeling 43,

Possible projects 6 Deterministic algorithms for approximate inference 7 Hugin architecture: potentials in the cliques are changed dynamically and there’s a division in the separators Lauritzen and Spiegelhalter (1988) F.Jensen, S.Lauritzen, K.Olesen (1990) Bayesian updating in causal probabilistic networks by local computations. Computational Statistics Quarterly 4, Lazy propagation: dissolves the differences between Shenoy-Shafer and Hugin propagation A.Madsen, F.Jensen (1999) Lazy evaluation of symmetric Bayesian decision problems, UAI-99, More on graph theory: properties of c.i., equivalence graph-lists of c.i. statements-factorization of the JPD Chaps 5 (5.3, 5.4, 5.6) and 6 of Castillo et al’s book Chap5 of Jensen’s book