176 citations · 683 across the 36 of their papers we have counts for
7 papers · 1 filter
Efficient Multifidelity Likelihood-Free Bayesian Inference with Adaptive Computational Resource Allocation
Thomas P Prescott, David J Warne, Ruth E Baker
Likelihood-free Bayesian inference algorithms are popular methods for calibrating the parameters of complex, stochastic models, required when the likelihood of the observed data is…
Measuring the accuracy of likelihood-free inference
Aden Forrow, Ruth E. Baker
Complex scientific models where the likelihood cannot be evaluated present a challenge for statistical inference. Over the past two decades, a wide range of algorithms have been pr…
Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling
Thomas P. Prescott, Ruth E. Baker
Multifidelity approximate Bayesian computation (MF-ABC) is a likelihood-free technique for parameter inference that exploits model approximations to significantly increase the spee…
Rapid Bayesian inference for expensive stochastic models
David J. Warne, Ruth E. Baker, Matthew J. Simpson
Almost all fields of science rely upon statistical inference to estimate unknown parameters in theoretical and computational models. While the performance of modern computer hardwa…
Multifidelity Approximate Bayesian Computation
Thomas P Prescott, Ruth E Baker
A vital stage in the mathematical modelling of real-world systems is to calibrate a model's parameters to observed data. Likelihood-free parameter inference methods, such as Approx…
An automatic adaptive method to combine summary statistics in approximate Bayesian computation
Jonathan U Harrison, Ruth E Baker
To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the mai…
Multilevel rejection sampling for approximate Bayesian computation
David J. Warne, Ruth E. Baker, Matthew J. Simpson
Likelihood-free methods, such as approximate Bayesian computation, are powerful tools for practical inference problems with intractable likelihood functions. Markov chain Monte Car…