Add to ORKGThis paper presents Variational Message Passing (VMP), a general purpose algorithm for applying variational inference to a Bayesian Network. Like belief propagation, Variational Message Passing proceeds by passing messages between nodes in the graph and updating posterior beliefs using local operations at each node. Each such update increases a lower bound on the log evidence (unless already at a local maximum). In contrast to belief propagation, VMP can be applied to a very general class of conjugate-exponential models because it uses a factorised variational approximation. Furthermore, by introducing ad-ditional variational parameters, VMP can be applied to models containing non-conjugate distributions. The VMP framework also allows the l...
The aim of Probabilistic Programming (PP) is to automate inference in probabilistic models. One effi...
We propose a new class of learning algorithms that combines variational approximation and Markov cha...
Accurate evaluation of Bayesian model evidence for a given data set is a fundamental problem in mode...
Variational Message Passing (VMP) provides an automatable and efficient algorithmic framework for ap...
Variational message passing is an efficient Bayesian inference method in factorized probabilistic mo...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
University of Technology Sydney. Faculty of Science.Message passing algorithms are a group of fast, ...
Variational Message Passing facilitates automated variational inference in factorized probabilistic ...
Variational approximations are becoming a widespread tool for Bayesian learning of graphical models....
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous va...
Neuronal computations rely upon local interactions across synapses. For a neuronal network to perfor...
The aim of Probabilistic Programming (PP) is to automate inference in probabilistic models. One effi...
We propose a new class of learning algorithms that combines variational approximation and Markov cha...
Accurate evaluation of Bayesian model evidence for a given data set is a fundamental problem in mode...
Variational Message Passing (VMP) provides an automatable and efficient algorithmic framework for ap...
Variational message passing is an efficient Bayesian inference method in factorized probabilistic mo...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
University of Technology Sydney. Faculty of Science.Message passing algorithms are a group of fast, ...
Variational Message Passing facilitates automated variational inference in factorized probabilistic ...
Variational approximations are becoming a widespread tool for Bayesian learning of graphical models....
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous va...
Neuronal computations rely upon local interactions across synapses. For a neuronal network to perfor...
The aim of Probabilistic Programming (PP) is to automate inference in probabilistic models. One effi...
We propose a new class of learning algorithms that combines variational approximation and Markov cha...
Accurate evaluation of Bayesian model evidence for a given data set is a fundamental problem in mode...