PROJECTS ON INTRODUCTION AND INFERENCE

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

2. Possible projects 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: Canonical models for the CPTs: Noisy OR modelisations 1 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-93 3.3 in Neapolitan’s book 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) 2 C.Boutilier, N.Friedman, M.Goldszmidt, D.Koller (1996) Context-specific independence in Bayesian networks. UAI-96, 115-123

3. Possible projects Modeling tricks: parent divorcing, time-stamped models, expert disagreements, interventions… 3 2.3 in Jensen’s book Abductive inference 4 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, 101-120 Importance sampling for approximate inference 5 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, 53-91 C.Yuan, M.Druzdzel (2005) Importance sampling algorithms for Bayesian networks: Principles and performance, Mathematical and Computer Modeling 43,1189-1207

4. Possible projects Deterministic algorithms for approximate inference 6 Hugin architecture: potentials in the cliques are changed dynamically and there’s a division in the separators 7 Lauritzen and Spiegelhalter (1988) F.Jensen, S.Lauritzen, K.Olesen (1990) Bayesian updating in causal probabilistic networks by local computations. Computational Statistics Quarterly 4, 269-282 Lazy propagation: dissolves the differences between Shenoy-Shafer and Hugin propagation 8 A.Madsen, F.Jensen (1999) Lazy evaluation of symmetric Bayesian decision problems, UAI-99, 382-390 More on graph theory: properties of c.i., equivalence graph-lists of c.i. statements-factorization of the JPD 9 Chaps 5 (5.3, 5.4, 5.6) and 6 of Castillo et al’s book Chap5 of Jensen’s book