likelihood-free nested sampling

Last week, I came by chance across a paper by Jan Mikelson and Mustafa Khammash on a likelihood-free version of nested sampling (a popular keyword on the ‘Og!). Published in 2020 in PLoS Comput Biol. The setup is a parameterised and hidden state-space model, which allows for an approximation of the (observed) likelihood function L(θ|y) […]

likelihood-free and summary-free?

My friends and coauthors Chris Drovandi and David Frazier have recently arXived a paper entitled A comparison of likelihood-free methods with and without summary statistics. In which they indeed compare these two perspectives on approximate Bayesian methods like ABC and Bayesian synthetic likelihoods. “A criticism of summary statistic based approaches is that their choice is […]

likelihood-free inference by ratio estimation

“This approach for posterior estimation with generative models mirrors the approach of Gutmann and Hyvärinen (2012) for the estimation of unnormalised models. The main difference is that here we classify between two simulated data sets while Gutmann and Hyvärinen (2012) classified between the observed data and simulated reference data.” A 2018 arXiv posting by Owen […]

likelihood-free Bayesian design [SimStat 2019 discussion]

likelihood-free approximate Gibbs sampling

“Low-dimensional regression-based models are constructed for each of these conditional distributions using synthetic (simulated) parameter value and summary statistic pairs, which then permit approximate Gibbs update steps (…) synthetic datasets are not generated during each sampler iteration, thereby providing efficiencies for expensive simulator models, and only require sufficient synthetic datasets to adequately construct the full […]