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tdivergence Based Approximate Inference
"... Approximate inference is an important technique for dealing with large, intractable graphical models based on the exponential family of distributions. We extend the idea of approximate inference to the texponential family by defining a new tdivergence. This divergence measure is obtained via conve ..."
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Approximate inference is an important technique for dealing with large, intractable graphical models based on the exponential family of distributions. We extend the idea of approximate inference to the texponential family by defining a new tdivergence. This divergence measure is obtained via
Gaussian KullbackLeibler Approximate Inference
"... We investigate Gaussian KullbackLeibler (GKL) variational approximate inference techniques for Bayesian generalised linear models and various extensions. In particular we make the following novel contributions: sufficient conditions for which the GKL objective is differentiable and convex are des ..."
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Cited by 3 (0 self)
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We investigate Gaussian KullbackLeibler (GKL) variational approximate inference techniques for Bayesian generalised linear models and various extensions. In particular we make the following novel contributions: sufficient conditions for which the GKL objective is differentiable and convex
Approximate Inference and Sidechain Prediction
"... Sidechain prediction is an important subtask in the proteinfolding problem. We show that finding a minimal energy sidechain configuration is equivalent to performing inference in an undirected graphical model. The graphical model is relatively sparse yet has many cycles. We used this equivalence ..."
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Cited by 3 (1 self)
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to assess the performance of approximate inference algorithms in a realworld setting. Specifically, we were interested in two questions: (1) which approximate inference algorithms give superior performance and (2) how does this performance compare to the stateoftheart in computational biology. We looked
Approximate inference and stochastic optimal control
, 2013
"... We propose a novel reformulation of the stochastic optimal control problem as an approximate inference problem, demonstrating, that such a interpretation leads to new practical methods for the original problem. In particular we characterise a novel class of iterative solutions to the stochastic opti ..."
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Cited by 4 (0 self)
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We propose a novel reformulation of the stochastic optimal control problem as an approximate inference problem, demonstrating, that such a interpretation leads to new practical methods for the original problem. In particular we characterise a novel class of iterative solutions to the stochastic
Approximate Inference in Collective Graphical Models
, 2013
"... We study the problem of approximate inference in collective graphical models (CGMs), which were recently introduced to model the problem of learning and inference with noisy aggregate observations. We first analyze the complexity of inference in CGMs: unlike inference in conventional graphical model ..."
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Cited by 5 (5 self)
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We study the problem of approximate inference in collective graphical models (CGMs), which were recently introduced to model the problem of learning and inference with noisy aggregate observations. We first analyze the complexity of inference in CGMs: unlike inference in conventional graphical
1 The DLR Hierarchy of Approximate Inference
"... We propose a hierarchy for approximate inference based on the Dobrushin, Lanford, Ruelle (DLR) equations. This hierarchy includes existing algorithms, such as belief propagation, and also motivates novel algorithms such as factorized neighbors (FN) algorithms and variants of mean field (MF) algorith ..."
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We propose a hierarchy for approximate inference based on the Dobrushin, Lanford, Ruelle (DLR) equations. This hierarchy includes existing algorithms, such as belief propagation, and also motivates novel algorithms such as factorized neighbors (FN) algorithms and variants of mean field (MF
Approximate Inference by Compilation to Arithmetic Circuits
"... Arithmetic circuits (ACs) exploit contextspecific independence and determinism to allow exact inference even in networks with high treewidth. In this paper, we introduce the first ever approximate inference methods using ACs, for domains where exact inference remains intractable. We propose and eva ..."
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Cited by 5 (4 self)
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Arithmetic circuits (ACs) exploit contextspecific independence and determinism to allow exact inference even in networks with high treewidth. In this paper, we introduce the first ever approximate inference methods using ACs, for domains where exact inference remains intractable. We propose
Variational algorithms for approximate Bayesian inference
, 2003
"... The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coherent way, avoids overfitting problems, and provides a principled basis for selecting between alternative models. Unfortunately the computations required are usually intractable. This thesis presents ..."
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Cited by 440 (9 self)
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a unified variational Bayesian (VB) framework which approximates these computations in models with latent variables using a lower bound on the marginal likelihood. Chapter 1 presents background material on Bayesian inference, graphical models, and propagation algorithms. Chapter 2 forms
Approximate Inference in Gaussian Graphical Models
, 2008
"... The focus of this thesis is approximate inference in Gaussian graphical models. A graphical model is a family of probability distributions in which the structure of interactions among the random variables is captured by a graph. Graphical models have become a powerful tool to describe complex highd ..."
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Cited by 5 (0 self)
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The focus of this thesis is approximate inference in Gaussian graphical models. A graphical model is a family of probability distributions in which the structure of interactions among the random variables is captured by a graph. Graphical models have become a powerful tool to describe complex high
Expectation propagation for approximate inference in dynamic Bayesian networks
 In Proceedings UAI
, 2002
"... We describe expectation propagation for approximate inference in dynamic Bayesian networks as a natural extension of Pearl's exact belief propagation. Expectation propagation is a greedy algorithm, converges in many practical cases, but not always. We derive a doubleloop algorithm, guaranteed ..."
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Cited by 56 (11 self)
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We describe expectation propagation for approximate inference in dynamic Bayesian networks as a natural extension of Pearl's exact belief propagation. Expectation propagation is a greedy algorithm, converges in many practical cases, but not always. We derive a doubleloop algorithm, guaranteed
Results 21  30
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