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deep and embarrassingly parallel MCMC

April 9, 2019

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 […]

parallelizable sampling method for parameter inference of large biochemical reaction models

June 18, 2018

I came across this older (2016) arXiv paper by Jan Mikelson and Mustafa Khammash [antidated as of April 25, 2018] as another version of nested sampling. The novelty of the approach is in applying nested sampling for approximating the likelihood function in the case of involved hidden Markov models (although the name itself does not […]

parallel adaptive importance sampling

August 30, 2016

Following Paul Russell’s talk at MCqMC 2016, I took a look at his recently arXived paper. In the plane to Sydney. The pseudo-code representation of the method is identical to our population Monte Carlo algorithm as is the suggestion to approximate the posterior by a mixture, but one novel aspect is to use Reich’s ensemble […]

Optimization Monte Carlo: Efficient and embarrassingly parallel likelihood-free inference

December 16, 2015

Ted Meeds and Max Welling have not so recently written about an embarrassingly parallel approach to ABC that they call optimisation Monte Carlo. [Danke Ingmar for pointing out the reference to me.] They start from a rather innocuous rephrasing of the ABC posterior, writing the pseudo-observations as deterministic transforms of the parameter and of a […]

parallelizing MCMC with random partition trees

July 7, 2015

Another arXived paper in the recent series about big or tall data and how to deal with it by MCMC. Which pertains to the embarrassingly parallel category. As in the previously discussed paper, the authors (Xiangyu Wang, Fangjian Guo, Katherine Heller, and David Dunson) chose to break the prior itself into m bits… (An additional […]