Name
Abstract | ||
|---|---|---|
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for tile marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large sampies of data. Tile standard BIC as well as our extension punishes the complexity of a model according to tile dimension of its parameters. We argue that the dimension of a Bayesian uetwork with hidden variables is tile rank of the Jacobian matrix of the transformation between the parameters of the network and the parameters of the observable variables. We compute the dimensions of several networks including the naive Bayes model with a hidden root node. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-94-011-5014-9_16 | Learning in graphical models |
Keywords | DocType | Volume |
hidden root node,bayesian uetwork,bayesian network,tile marginal likelihood,tile rank,asymptotic approximation,tile standard bic,hidden variable,tile dimension,asymptotic model selection,bayesian information criterion,model selection,hidden variables,jacobian matrix,marginal likelihood | Journal | abs/1302.3580 |
ISSN | ISBN | Citations |
0258-123X | 0-262-60032-3 | 38 |
PageRank | References | Authors |
16.77 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Geiger | 1 | 3371 | 570.49 |
| David Heckerman | 2 | 6951 | 1419.21 |
| Christopher Meek | 3 | 1770 | 248.06 |