Abstract. A key problem in statistics and machine learning is inferring suitable structure of a model given some observed data. A Bayesian approach to model comparison makes use of the marginal likelihood of each candidate model to form a posterior distribution over models; unfortunately for most models of interest, notably those containing hidden or latent variables, the marginal likelihood is intractable to compute. We present the variational Bayesian (VB) algorithm for directed graphical models, which optimises a lower bound approximation to the marginal likelihood in a procedure similar to the standard EM algorithm. We show that for a large class of models, which we call conjugate exponential, the VB algorithm is a straightforward gener...
Abstract—In this paper we present an introduction to Vari-ational Bayesian (VB) methods in the conte...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Hidden Markov random field models provide an appealing representation of images and other spatial pr...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
We present an efficient procedure for estimating the marginal likelihood of probabilistic models wit...
Variational approximations are becoming a widespread tool for Bayesian learning of graphical models....
Abstract. We discuss Bayesian methods for model averaging and model selection among Bayesian-network...
The variational approach to Bayesian inference enables simultaneous estimation of model parameters a...
The variational approach to Bayesian inference enables simultaneous estimation of model parameters a...
This paper applies the variational methods to learn the parameters and the probability of evidence o...
We discuss Bayesian methods for model averaging and model selection among Bayesiannetwork models wit...
Graphical models are a general-purpose tool for modeling complex distributions in a way which facili...
Bayesian learning in undirected graphical models—computing posterior distributions over parameters a...
A Bayesian factor graph reduced to normal form consists in the interconnection of diverter units (o...
Bayesian-directed acyclic discrete-variable graphs are reduced to a simplified normal form made up o...
Abstract—In this paper we present an introduction to Vari-ational Bayesian (VB) methods in the conte...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Hidden Markov random field models provide an appealing representation of images and other spatial pr...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
We present an efficient procedure for estimating the marginal likelihood of probabilistic models wit...
Variational approximations are becoming a widespread tool for Bayesian learning of graphical models....
Abstract. We discuss Bayesian methods for model averaging and model selection among Bayesian-network...
The variational approach to Bayesian inference enables simultaneous estimation of model parameters a...
The variational approach to Bayesian inference enables simultaneous estimation of model parameters a...
This paper applies the variational methods to learn the parameters and the probability of evidence o...
We discuss Bayesian methods for model averaging and model selection among Bayesiannetwork models wit...
Graphical models are a general-purpose tool for modeling complex distributions in a way which facili...
Bayesian learning in undirected graphical models—computing posterior distributions over parameters a...
A Bayesian factor graph reduced to normal form consists in the interconnection of diverter units (o...
Bayesian-directed acyclic discrete-variable graphs are reduced to a simplified normal form made up o...
Abstract—In this paper we present an introduction to Vari-ational Bayesian (VB) methods in the conte...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Hidden Markov random field models provide an appealing representation of images and other spatial pr...