
Bayesian Detectability of Induced Polarisation in Airborne Electromagnetic Data using Reversible Jump Sequential Monte Carlo
Detection of induced polarisation (IP) effects in airborne electromagnet...
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Modelling and predicting soil carbon sequestration: is current model structure fit for purpose?
Soil carbon accounting and prediction play a key role in building decisi...
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Synthetic Likelihood in Misspecified Models: Consequences and Corrections
We analyse the behaviour of the synthetic likelihood (SL) method when th...
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A Comparison of LikelihoodFree Methods With and Without Summary Statistics
Likelihoodfree methods are useful for parameter estimation of complex m...
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Accelerating sequential Monte Carlo with surrogate likelihoods
Delayedacceptance is a technique for reducing computational effort for ...
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Robust Approximate Bayesian Computation: An Adjustment Approach
We propose a novel approach to approximate Bayesian computation (ABC) th...
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Transformations in SemiParametric Bayesian Synthetic Likelihood
Bayesian synthetic likelihood (BSL) is a popular method for performing a...
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Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
Bayesian experimental design (BED) is a framework that uses statistical ...
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Efficient Bayesian synthetic likelihood with whitening transformations
Likelihoodfree methods are an established approach for performing appro...
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Estimating a novel stochastic model for withinfield disease dynamics of banana bunchy top virus via approximate Bayesian computation
The Banana Bunchy Top Virus (BBTV) is one of the most economically impor...
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Particle Methods for Stochastic Differential Equation Mixed Effects Models
Parameter inference for stochastic differential equation mixed effects m...
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BSL: An R Package for Efficient Parameter Estimation for SimulationBased Models via Bayesian Synthetic Likelihood
Bayesian synthetic likelihood (BSL) is a popular method for estimating t...
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Efficient Bayesian estimation for GARCHtype models via Sequential Monte Carlo
This paper exploits the advantages of sequential Monte Carlo (SMC) to de...
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Ensemble MCMC: Accelerating PseudoMarginal MCMC for State Space Models using the Ensemble Kalman Filter
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for pe...
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Robust Approximate Bayesian Inference with Synthetic Likelihood
Bayesian synthetic likelihood (BSL) is now a wellestablished method for...
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Acceleration of expensive computations in Bayesian statistics using vector operations
Many applications in Bayesian statistics are extremely computationally i...
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Bayesian inference using synthetic likelihood: asymptotics and adjustments
Implementing Bayesian inference is often computationally challenging in ...
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Regularised ZeroVariance Control Variates for HighDimensional Variance Reduction
Zerovariance control variates (ZVCV) are a postprocessing method to r...
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Regularised ZeroVariance Control Variates
Zerovariance control variates (ZVCV) is a postprocessing method to re...
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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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Optimal Bayesian design for model discrimination via classification
Performing optimal Bayesian design for discriminating between competing ...
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Christopher Drovandi
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