|
717
|
Bayes Factors
– Robert E. Kass, Adrian E. Raftery
- 1995
|
|
1581
|
Estimating the dimension of a model
– G E Schwarz
- 1978
|
|
215
|
Model selection and accounting for model uncertainty in graphical models using Occam's window
– David Madigan, Adrian E. Raftery
- 1993
|
|
788
|
A: Bayesian Theory
– J Bernardo, Smith
- 1994
|
|
168
|
Bayesian Graphical Models for Discrete Data
– David Madigan, Jeremy York
- 1993
|
|
752
|
Learning Bayesian networks: The combination of knowledge and statistical data
– David Heckerman, David M. Chickering
- 1995
|
|
177
|
Sequential updating of conditional probabilities on directed graphical structures
– D J Spiegelhalter, S L Lauritzen
- 1990
|
|
37
|
Asymptotic model selection for directed networks with hidden variables
– Dan Geiger, David Heckerman, Christopher Meek
- 1996
|
|
384
|
Causation, prediction and search
– P Spirtes, C Glymour, R Scheines
- 1993
|
|
3012
|
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
– S Geman, D Geman
- 1984
|
|
449
|
Probabilistic Inference Using Markov Chain Monte Carlo Methods
– Radford M. Neal
- 1993
|
|
6234
|
Maximum likelihood from incomplete data via the EM algorithm
– A. P. Dempster, N. M. Laird, D. B. Rubin
- 1977
|
|
295
|
Bayesian computations via the Gibbs sampler and related Markov chain Monte Carlo methods
– A F M Smith, G O Roberts
- 1993
|
|
25
|
Causal Inference in the Presence of Latent Variables and Selection Bias
– Peter Spirtes, Christopher Meek, Thomas Richardson
|
|
841
|
Information theory and extension of the maximum likelihood principle
– H Akaike
- 1973
|
|
47
|
Learning bayesian networks with discrete variables from data
– P Spirtes, C Meek
- 1995
|
|
8
|
Likelihoods and priors for Bayesian networks
– D Heckerman, D Geiger
- 1995
|
|
14
|
Asymptotics in Bayesian computation
– R E Kass, L Tierney, J B Kadane
- 1988
|
|
26
|
Stochastic Complexity" (with discussion
– J J Rissanen
- 1987
|
