research
∙
07/21/2023
Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC^2
Sequential Monte Carlo squared (SMC^2; Chopin et al., 2012) methods can ...
share
research
∙
07/21/2023
Unlocking ensemble ecosystem modelling for large and complex networks
The potential effects of conservation actions on threatened species can ...
research
∙
05/24/2023
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood
The Bayesian Synthetic Likelihood (BSL) method is a widely-used tool for...
research
∙
05/18/2023
Generalised likelihood profiles for models with intractable likelihoods
Likelihood profiling is an efficient and powerful frequentist approach f...
research
∙
05/15/2023
Bayesian inference for misspecified generative models
Bayesian inference is a powerful tool for combining information in compl...
research
∙
05/09/2023
Bayesian Synthetic Likelihood
Bayesian statistics is concerned with conducting posterior inference for...
research
∙
02/13/2023
Reliable Bayesian Inference in Misspecified Models
We provide a general solution to a fundamental open problem in Bayesian ...
research
∙
12/05/2022
Better Together: pooling information in likelihood-free inference
Likelihood-free inference (LFI) methods, such as Approximate Bayesian co...
research
∙
11/18/2022
Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Building on a strong foundation of philosophy, theory, methods and compu...
research
∙
11/10/2022
Bayesian score calibration for approximate models
Scientists continue to develop increasingly complex mechanistic models t...
research
∙
10/22/2022
Transport Reversible Jump Proposals
Reversible jump Markov chain Monte Carlo (RJMCMC) proposals that achieve...
research
∙
08/08/2022
Monte Carlo twisting for particle filters
We consider the problem of designing efficient particle filters for twis...
research
∙
07/21/2022
Efficient inference and identifiability analysis for differential equation models with random parameters
Heterogeneity is a dominant factor in the behaviour of many biological p...
research
∙
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...
research
∙
06/06/2022
Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance
The ensemble Kalman filter (EnKF) is a Monte Carlo approximation of the ...
research
∙
05/24/2022
Many-levelled continuation ratio models for frequency of alcohol and drug use data
Studies of alcohol and drug use are often interested in the number of da...
research
∙
04/12/2022
Strategic model reduction by analysing model sloppiness: a case study in coral calcification
It can be difficult to identify ways to reduce the complexity of large m...
research
∙
03/29/2022
Analysis of sloppiness in model simulations: unveiling parameter uncertainty when mathematical models are fitted to data
This work introduces a Bayesian approach to assess the sensitivity of mo...
research
∙
03/18/2022
Modularized Bayesian analyses and cutting feedback in likelihood-free inference
There has been much recent interest in modifying Bayesian inference for ...
research
∙
02/04/2022
Population Calibration using Likelihood-Free Bayesian Inference
In this paper we develop a likelihood-free approach for population calib...
research
∙
01/27/2022
Adaptive exact-approximate sequential Monte Carlo
Exact-approximate sequential Monte Carlo (SMC) methods target the exact ...
research
∙
01/05/2022
The effect of biologically mediated decay rates on modelling soil carbon sequestration in agricultural settings
Microbial biomass carbon (MBC), a crucial soil labile carbon fraction, i...
research
∙
09/02/2021
Bayesian Detectability of Induced Polarisation in Airborne Electromagnetic Data using Reversible Jump Sequential Monte Carlo
Detection of induced polarisation (IP) effects in airborne electromagnet...
research
∙
05/11/2021
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...
research
∙
04/08/2021
Synthetic Likelihood in Misspecified Models: Consequences and Corrections
We analyse the behaviour of the synthetic likelihood (SL) method when th...
research
∙
03/03/2021
A Comparison of Likelihood-Free Methods With and Without Summary Statistics
Likelihood-free methods are useful for parameter estimation of complex m...
research
∙
09/08/2020
Accelerating sequential Monte Carlo with surrogate likelihoods
Delayed-acceptance is a technique for reducing computational effort for ...
research
∙
08/07/2020
Robust Approximate Bayesian Computation: An Adjustment Approach
We propose a novel approach to approximate Bayesian computation (ABC) th...
research
∙
07/03/2020
Transformations in Semi-Parametric Bayesian Synthetic Likelihood
Bayesian synthetic likelihood (BSL) is a popular method for performing a...
research
∙
03/20/2020
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
Bayesian experimental design (BED) is a framework that uses statistical ...
research
∙
09/11/2019
Efficient Bayesian synthetic likelihood with whitening transformations
Likelihood-free methods are an established approach for performing appro...
research
∙
09/05/2019
Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation
The Banana Bunchy Top Virus (BBTV) is one of the most economically impor...
research
∙
07/25/2019
Particle Methods for Stochastic Differential Equation Mixed Effects Models
Parameter inference for stochastic differential equation mixed effects m...
research
∙
07/25/2019
BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood
Bayesian synthetic likelihood (BSL) is a popular method for estimating t...
research
∙
06/10/2019
Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo
This paper exploits the advantages of sequential Monte Carlo (SMC) to de...
research
∙
06/05/2019
Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for pe...
research
∙
04/09/2019
Robust Approximate Bayesian Inference with Synthetic Likelihood
Bayesian synthetic likelihood (BSL) is now a well-established method for...
research
∙
02/25/2019
Acceleration of expensive computations in Bayesian statistics using vector operations
Many applications in Bayesian statistics are extremely computationally i...
research
∙
02/13/2019
Bayesian inference using synthetic likelihood: asymptotics and adjustments
Implementing Bayesian inference is often computationally challenging in ...
research
∙
11/13/2018
Regularised Zero-Variance Control Variates for High-Dimensional Variance Reduction
Zero-variance control variates (ZV-CV) are a post-processing method to r...
research
∙
11/13/2018
Regularised Zero-Variance Control Variates
Zero-variance control variates (ZV-CV) is a post-processing method to re...
research
∙
09/16/2018
Robust Bayesian Synthetic Likelihood via a Semi-Parametric Approach
Bayesian synthetic likelihood (BSL) is now a well established method for...
research
∙
09/14/2018
