“…construction of low dimensional summary statistics can be performed as in a black box…” Today Zhou and Fukuzumi just arXived a paper that proposes a gradient-based dimension reduction for ABC summary statistics, in the spirit of RKHS kernels as advocated, e.g., by Arthur Gretton. Here the projection is a mere linear projection Bs of the […]

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## local kernel reduction for ABC

September 14, 2016## astroABC: ABC SMC sampler for cosmological parameter estimation

September 6, 2016“…the chosen statistic needs to be a so-called sufficient statistic in that any information about the parameter of interest which is contained in the data, is also contained in the summary statistic.” Elise Jenningsa and Maeve Madigan arXived a paper on a new Python code they developed for implementing ABC-SMC, towards astronomy or rather cosmology […]

## ABC by subset simulation

August 25, 2016Last week, Vakilzadeh, Beck and Abrahamsson arXived a paper entitled “Using Approximate Bayesian Computation by Subset Simulation for Efficient Posterior Assessment of Dynamic State-Space Model Classes”. It follows an earlier paper by Beck and co-authors on ABC by subset simulation, paper that I did not read. The model of interest is a hidden Markov model […]

## automatic variational ABC

July 8, 2016“Stochastic Variational inference is an appealing alternative to the inefficient sampling approaches commonly used in ABC.” Moreno et al. [including Ted Meeds and Max Welling] recently arXived a paper merging variational inference and ABC. The argument for turning variational is computational speedup. The traditional (in variational inference) divergence decomposition of the log-marginal likelihood is replaced […]

## ABC random forests for Bayesian parameter inference [version 2.0]

June 30, 2016Just mentioning that a second version of our paper has been arXived and submitted to JMLR, the main input being the inclusion of a reference to the abcrf package. And just repeating our best selling arguments that (i) forests do not require a preliminary selection of the summary statistics, since an arbitrary number of summaries […]