Category: curse of dimensionality

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 […]

selecting summary statistics [a tale of two distances]

As Jonathan Harrison came to give a seminar in Warwick [which I could not attend], it made me aware of his paper with Ruth Baker on the selection of summaries in ABC. The setting is an ABC-SMC algorithm and it relates with Fearnhead and Prangle (2012), Barnes et al. (2012), our own random forest approach, […]

deep and embarrassingly parallel MCMC

Diego Mesquita, Paul Blomstedt, and Samuel Kaski (from Helsinki, like the above picture) just arXived a paper on embarrassingly parallel MCMC. Following a series of papers discussed on this ‘og in the past. They use a deep learning approach of Dinh et al. (2017) to the computation of the probability density of a convoluted and […]

adaptive copulas for ABC

A paper on ABC I read on my way back from Cambodia:  Yanzhi Chen and Michael Gutmann arXived an ABC [in Edinburgh] paper on learning the target via Gaussian copulas, to be presented at AISTATS this year (in Okinawa!). Linking post-processing (regression) ABC and sequential ABC. The drawback in the regression approach is that the […]

approximate likelihood perspective on ABC

George Karabatsos and Fabrizio Leisen have recently published in Statistics Surveys a fairly complete survey on ABC methods [which earlier arXival I had missed]. Listing within an extensive bibliography of 20 pages some twenty-plus earlier reviews on ABC (with further ones in applied domains)! “(…) any ABC method (algorithm) can be categorized as either (1) […]