At the Bayes, Fiducial and Frequentist workshop last month, I discussed with the authors of this newly arXived paper, Approximate confidence distribution computing, Suzanne Thornton and Min-ge Xie. Which they abbreviate as ACC and not as ACDC. While I have discussed the notion of confidence distribution in some earlier posts, this paper aims at producing […]

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## ACDC versus ABC

June 12, 2017## fast ε-free ABC

June 8, 2017Last Fall, George Papamakarios and Iain Murray from Edinburgh arXived an ABC paper on fast ε-free inference on simulation models with Bayesian conditional density estimation, paper that I missed. The idea there is to approximate the posterior density by maximising the likelihood associated with a parameterised family of distributions on θ, conditional on the associated […]

## efficient acquisition rules for ABC

June 5, 2017A few weeks ago, Marko Järvenpää, Michael Gutmann, Aki Vehtari and Pekka Marttinen arXived a paper on sampling design for ABC that reminded me of presentations Michael gave at NIPS 2014 and in Banff last February. The main notion is that, when the simulation from the model is hugely expensive, random sampling does not make […]

## postprocessing for ABC

June 1, 2017Two weeks ago, G.S. Rodrigues, Dennis Prangle and Scott Sisson have recently arXived a paper on recalibrating ABC output to make it correctly calibrated (in the frequentist sense). As in earlier papers, it takes advantage of the fact that the tail posterior probability should be uniformly distributed at the true value of the [simulated] parameter […]

## ABCπ

May 17, 2017Ritabrata Dutta, Marcel Schöengens, Jukka-Pekka Onnela, and Antonietta Mira recently put a new ABC software on-line, called ABCpy for ABC with Python. The software aims at an automated parallelisation of ABC runs, requiring only code to generate from the (generative) model and the choice of summary statistics and of associated distance. Alternatively an approximate likelihood […]