kevin murphy rxysyew9gmailcom pleased announce release bdagl pronounced bedaggle matlabc package learning bayes net structures fully observed data discrete continuous static time series main novelty implements various algorithms exact bayesian inference posterior features modes using dynamic programming also supports mcmc dags orders various proposal distributions details given url feedback welcome kevin murphy httpwwwcsubccamurphyksoftwarebdaglindexhtml major features computes probable graph g_map arg max_g pgd exactly using dynamic programming dp algorithm silander myllymaki uai06 g dag data takes od 2d time space limited 20 variables takes 5 seconds d10 5 minutes d20 computes exact edge marginals using bayes model averaging pg_ij1d sum_g igij1 pgd using dp algorithms koivisto sood jmlr04 koivisto uai06 takes od 2d time space limited 20 variables takes 5 seconds d10 5 minutes d20 computes edge marginals pg_ij1d posterior features approximately using mcmc space dags various proposal distributions options include standard local proposal add delete reverse edge global proposal based dp see eaton murphy uai07 also supports mcmc space total orders see friedman koller mlj03 gibbs sampling adjacency matrix principle algorithms avoid 2d bottleneck exact dp although current implementation may scale much beyond d20 supports various models intervention perfect imperfect uncertain soft learning causal networks experimental data see eaton murphy aistats07 details supports bde score multinomial models dirichlet priors bge score gaussian models gaussiangamma priors could extended flexible cpdspriors using bic supports dbn learning fully observed time series data supports posterior predictive density modeling test data integrating structures parameters also supports plugin approximation efficiently computes expected sufficient statistics discrete cpds large data sets using adtrees see moore lee jair98 thu 07 aug 2008 223020 0700 uai software release bayesian dag learning