“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 […]

## Search Results

## likelihood-free approximate Gibbs sampling

June 19, 2019## machine learning-based approach to likelihood-free inference

March 3, 2017At ABC’ory last week, Kyle Cranmer gave an extended talk on estimating the likelihood ratio by classification tools. Connected with a 2015 arXival. The idea is that the likelihood ratio is invariant by a transform s(.) that is monotonic with the likelihood ratio itself. It took me a few minutes (after the talk) to understand […]

## asymptotically exact inference in likelihood-free models [a reply from the authors]

December 1, 2016[Following my post of lastTuesday, Matt Graham commented on the paper with force détails. Here are those comments. A nicer HTML version of the Markdown reply below is also available on Github.] Thanks for the comments on the paper! A few additional replies to augment what Amos wrote: This however sounds somewhat intense in that […]

## asymptotically exact inference in likelihood-free models

November 29, 2016“We use the intuition that inference corresponds to integrating a density across the manifold corresponding to the set of inputs consistent with the observed outputs.” Following my earlier post on that paper by Matt Graham and Amos Storkey (University of Edinburgh), I now read through it. The beginning is somewhat unsettling, albeit mildly!, as it […]