Following a request from one of the reviewers of our chapter Likelihood-free model choice, I tried to run EP-ABC on a toy problem and to compare it with the outcome of a random forest ABC. Literally starting from scratch, namely from the description found in Simon and Nicolas’ JASA paper. To run my test, I […]

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## improved convergence of regression-adjusted ABC

October 7, 2016“These results highlight the tension in ABC between choices of the summary statistics and bandwidth that will lead to more accurate inferences when using the ABC posterior, against choices that will reduce the computational cost or Monte Carlo error of algorithms for sampling from the ABC posterior.” Wentao Li and Paul Fearnhead have arXived a […]

## local kernel reduction for ABC

September 14, 2016“…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 […]

## 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 […]