
Fast and Accurate Variational Inference for Models with Many Latent Variables
Models with a large number of latent variables are often used to fully u...
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Assessment and adjustment of approximate inference algorithms using the law of total variance
A common method for assessing validity of Bayesian sampling or approxima...
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Marginallycalibrated deep distributional regression
Deep neural network (DNN) regression models are widely used in applicati...
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Conditionally structured variational Gaussian approximation with importance weights
We develop flexible methods of deriving variational inference for models...
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Highdimensional copula variational approximation through transformation
Variational methods are attractive for computing Bayesian inference for ...
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Using prior expansions for priordata conflict checking
Any Bayesian analysis involves combining information represented through...
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Bayesian inference using synthetic likelihood: asymptotics and adjustments
Implementing Bayesian inference is often computationally challenging in ...
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An easytouse empirical likelihood ABC method
Many scientifically wellmotivated statistical models in natural, engine...
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Robust Bayesian Synthetic Likelihood via a SemiParametric Approach
Bayesian synthetic likelihood (BSL) is now a well established method for...
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Gaussian variational approximation for highdimensional state space models
Our article considers variational approximations of the posterior distri...
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New Insights into History Matching via Sequential Monte Carlo
The aim of the history matching method is to locate nonimplausible regi...
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Gaussian variational approximation with sparse precision matrices
We consider the problem of learning a Gaussian variational approximation...
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Variational approximation for heteroscedastic linear models and matching pursuit algorithms
Modern statistical applications involving large data sets have focused a...
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David J. Nott
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