Add to ORKGThis tutorial describes the mean-field variational Bayesian approximation to inference in graphical models, using modern machine learning terminology rather than statistical physics concepts. It begins by seeking to find an approximate mean-field distribution close to the target joint in the KL-divergence sense. It then derives local node updates and reviews the recent Variational Message Passing framework. © Springer Science+Business Media B.V. 2011
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
The ill-posed nature of missing variable models offers a challenging testing ground for new computat...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
This paper presents Variational Message Passing (VMP), a general purpose algorithm for applying vari...
Abstract. The article describe the model, derivation, and implementation of variational Bayesian inf...
Variational methods for approximate Bayesian inference provide fast, flexible, deterministic alterna...
Variational approximations are becoming a widespread tool for Bayesian learning of graphical models....
Mean-field variational methods are widely used for approximate posterior inference in many prob-abil...
Mean-field variational methods are widely used for approximate posterior inference in many probabili...
We develop strategies for mean field variational Bayes approximate inference for Bayesian hierarchic...
Mean-field variational inference is a method for approximate Bayesian posterior inference. It approx...
<p>The variational Bayesian approach furnishes an approximation to the marginal posterior densities ...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
The ill-posed nature of missing variable models offers a challenging testing ground for new computat...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
This paper presents Variational Message Passing (VMP), a general purpose algorithm for applying vari...
Abstract. The article describe the model, derivation, and implementation of variational Bayesian inf...
Variational methods for approximate Bayesian inference provide fast, flexible, deterministic alterna...
Variational approximations are becoming a widespread tool for Bayesian learning of graphical models....
Mean-field variational methods are widely used for approximate posterior inference in many prob-abil...
Mean-field variational methods are widely used for approximate posterior inference in many probabili...
We develop strategies for mean field variational Bayes approximate inference for Bayesian hierarchic...
Mean-field variational inference is a method for approximate Bayesian posterior inference. It approx...
<p>The variational Bayesian approach furnishes an approximation to the marginal posterior densities ...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
The ill-posed nature of missing variable models offers a challenging testing ground for new computat...