Generation of Random Bayesian Networks with Constraints on Induced Width, with Application to the Average Analysis of d-Connectivity, Quasi-random Sampling, and Loopy Propagation Export

@inproceedings{ide2004daggeneration, abstract = {We present algorithms for the generation of uniformly distributed Bayesian networks with constraints on induced width. The algorithms use ergodic Markov chains to generate samples, building upon previous algorithms by the authors. The introduction of constraints on induced width leads to more realistic results but requires new techniques. We discuss three applications of randomly generated networks: we study the average number of nodes d-connected to a query, the effectiveness of quasi-random samples in approximate inference, and the convergence of loopy propagation for non-extreme distributions.}, author = {Ide, Jaime S. and Cozman, Fabio G. and Ramos, F\'{a}bio T. and Polit\'{e}cnica, Escola}, booktitle = {In Proceedings of the 16th Eureopean Conference on Artificial Intelligence}, citeulike-article-id = {4503465}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.6707}, keywords = {dag, generation}, pages = {323--327}, posted-at = {2009-05-11 20:11:53}, priority = {0}, title = {Generation of Random Bayesian Networks with Constraints on Induced Width, with Application to the Average Analysis of d-Connectivity, Quasi-random Sampling, and Loopy Propagation}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.6707}, volume = {2004}, year = {2004} }

TY - CONF ID - ide2004daggeneration L3 - citeulike-article-id:4503465 N2 - We present algorithms for the generation of uniformly distributed Bayesian networks with constraints on induced width. The algorithms use ergodic Markov chains to generate samples, building upon previous algorithms by the authors. The introduction of constraints on induced width leads to more realistic results but requires new techniques. We discuss three applications of randomly generated networks: we study the average number of nodes d-connected to a query, the effectiveness of quasi-random samples in approximate inference, and the convergence of loopy propagation for non-extreme distributions. EP - 327 TI - Generation of Random Bayesian Networks with Constraints on Induced Width, with Application to the Average Analysis of d-Connectivity, Quasi-random Sampling, and Loopy Propagation VL - 2004 T2 - In Proceedings of the 16th Eureopean Conference on Artificial Intelligence SP - 323 KW - dag KW - generation AU - Ide, Jaime AU - Cozman, Fabio AU - Ramos, Fábio AU - Politécnica, Escola PY - 2004/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.6707 ER -