© 2018 Australian Statistical Publishing Association Inc. Published by John Wiley & Sons Australia Pty Ltd. Expectation propagation is a general approach to deterministic approximate Bayesian inference for graphical models, although its literature is confined mostly to machine learning applications. We investigate the utility of expectation propagation in generalised, linear, and mixed model settings. We show that, even though the algebra and computations are complicated, the notion of message passing on factor graphs affords streamlining of the required calculations and we list the algorithmic steps explicitly. Numerical studies indicate expectation propagation is marginally more accurate than a competing method for the models considered, ...
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data...
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data...
We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood lea...
Factor graphs provide a convenient framework for automatically generating (approximate) Bayesian inf...
We discuss the expectation propagation (EP) algorithm for approximate Bayesian inference using a fac...
Expectation propagation (EP) is a novel variational method for approximate Bayesian inference, which...
© 2016, Institute of Mathematical Statistics. All rights reserved. We derive the explicit form of ex...
Analyzing latent Gaussian models by using approximate Bayesian inference methods has proven to be a ...
Abstract. We present a framework for efficient, accurate approximate Bayesian inference in generaliz...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear ...
We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference f...
Belief propagation (BP) on cyclic graphs is an efficient algorithm for computing approximate margina...
The research reported in this thesis focuses on approximation techniques for inference in graphical ...
University of Technology Sydney. Faculty of Science.Generalised linear mixed models are a particular...
Many models of interest in the natural and social sciences have no closed-form likelihood function, ...
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data...
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data...
We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood lea...
Factor graphs provide a convenient framework for automatically generating (approximate) Bayesian inf...
We discuss the expectation propagation (EP) algorithm for approximate Bayesian inference using a fac...
Expectation propagation (EP) is a novel variational method for approximate Bayesian inference, which...
© 2016, Institute of Mathematical Statistics. All rights reserved. We derive the explicit form of ex...
Analyzing latent Gaussian models by using approximate Bayesian inference methods has proven to be a ...
Abstract. We present a framework for efficient, accurate approximate Bayesian inference in generaliz...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear ...
We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference f...
Belief propagation (BP) on cyclic graphs is an efficient algorithm for computing approximate margina...
The research reported in this thesis focuses on approximation techniques for inference in graphical ...
University of Technology Sydney. Faculty of Science.Generalised linear mixed models are a particular...
Many models of interest in the natural and social sciences have no closed-form likelihood function, ...
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data...
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data...
We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood lea...