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05/15/2023
Bayesian inference for misspecified generative models
Bayesian inference is a powerful tool for combining information in compl...
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05/11/2023
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families
Even though dropout is a popular regularization technique, its theoretic...
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05/09/2023
Bayesian Synthetic Likelihood
Bayesian statistics is concerned with conducting posterior inference for...
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02/07/2023
Structured variational approximations with skew normal decomposable graphical models
Although there is much recent work developing flexible variational metho...
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01/31/2023
Misspecification-robust Sequential Neural Likelihood
Simulation-based inference (SBI) techniques are now an essential tool fo...
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01/26/2023
Guaranteed Accuracy of Semi-Modular Posteriors
Bayesian inference has widely acknowledged advantages in many problems, ...
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12/05/2022
Better Together: pooling information in likelihood-free inference
Likelihood-free inference (LFI) methods, such as Approximate Bayesian co...
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11/10/2022
Bayesian score calibration for approximate models
Scientists continue to develop increasingly complex mechanistic models t...
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07/14/2022
Improving the Accuracy of Marginal Approximations in Likelihood-Free Inference via Localisation
Likelihood-free methods are an essential tool for performing inference f...
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04/14/2022
Robust Bayesian inference in complex models with possibility theory
We propose a general solution to the problem of robust Bayesian inferenc...
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03/18/2022
Modularized Bayesian analyses and cutting feedback in likelihood-free inference
There has been much recent interest in modifying Bayesian inference for ...
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02/21/2022
Weakly informative priors and prior-data conflict checking for likelihood-free inference
Bayesian likelihood-free inference, which is used to perform Bayesian in...
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02/21/2022
Cutting feedback and modularized analyses in generalized Bayesian inference
Even in relatively simple settings, model misspecification can make the ...
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08/25/2021
Variational inference for cutting feedback in misspecified models
Bayesian analyses combine information represented by different terms in ...
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06/30/2021
Bayesian clustering using random effects models and predictive projections
Linear mixed models are widely used for analyzing hierarchically structu...
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05/04/2021
Variational Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models
Gaussian mixture models are a popular tool for model-based clustering, a...
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04/08/2021
Synthetic Likelihood in Misspecified Models: Consequences and Corrections
We analyse the behaviour of the synthetic likelihood (SL) method when th...
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11/12/2020
On a Variational Approximation based Empirical Likelihood ABC Method
Many scientifically well-motivated statistical models in natural, engine...
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10/15/2020
Detecting conflicting summary statistics in likelihood-free inference
Bayesian likelihood-free methods implement Bayesian inference using simu...
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10/05/2020
Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices
Recurrent neural networks (RNNs) with rich feature vectors of past value...
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05/15/2020
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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11/20/2019
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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08/26/2019
Marginally-calibrated deep distributional regression
Deep neural network (DNN) regression models are widely used in applicati...
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04/21/2019
Conditionally structured variational Gaussian approximation with importance weights
We develop flexible methods of deriving variational inference for models...
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04/16/2019
High-dimensional copula variational approximation through transformation
Variational methods are attractive for computing Bayesian inference for ...
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02/27/2019
Using prior expansions for prior-data conflict checking
Any Bayesian analysis involves combining information represented through...
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02/13/2019
Bayesian inference using synthetic likelihood: asymptotics and adjustments
Implementing Bayesian inference is often computationally challenging in ...
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10/03/2018
An easy-to-use empirical likelihood ABC method
Many scientifically well-motivated statistical models in natural, engine...
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09/16/2018
Robust Bayesian Synthetic Likelihood via a Semi-Parametric Approach
Bayesian synthetic likelihood (BSL) is now a well established method for...
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01/24/2018
Gaussian variational approximation for high-dimensional state space models
Our article considers variational approximations of the posterior distri...
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10/09/2017
New Insights into History Matching via Sequential Monte Carlo
The aim of the history matching method is to locate non-implausible regi...
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05/18/2016
Gaussian variational approximation with sparse precision matrices
We consider the problem of learning a Gaussian variational approximation...
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11/22/2010