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 […]

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## parallelizable sampling method for parameter inference of large biochemical reaction models

June 18, 2018I 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 […]