**I**n connection with the special issue of Entropy I mentioned a while ago, Pierre Alquier (formerly of CREST) has written an introduction to the topic of approximate Bayesian inference that is worth advertising (and freely-available as well). Its reference list is particularly relevant. (The deadline for submissions is 21 June,)

## Archive for Approximate Bayesian computation

## approximate Bayesian inference [survey]

Posted in Statistics with tags ABC, Approximate Bayesian computation, Bayesian statistics, CREST, entropy, expectation-propagation, Gibbs posterior, Langevin Monte Carlo, Laplace approximations, machine learning, Markov chain Monte Carlo, MCMC, PAC-Bayes, RIKEN, sequential Monte Carlo, special issue, survey, Tokyo, variational approximations on May 3, 2021 by xi'an## simulation-based inference for neuroscience [One World ABC seminar]

Posted in Books, pictures, Statistics, University life with tags ABC, Approximate Bayesian computation, neural network, neurosciences, numerical simulator, One World ABC Seminar, University of Warwick, webinar on April 26, 2021 by xi'an**T**he next One World ABC seminar will take place on Thursday at 11:30, UK time, and will broadcast a talk by Jakob Macke on *Simulation-based inference for neuroscience*. Here is the abstract

Neuroscience research makes extensive use of mechanistic models of neural dynamics — these models are often implemented through numerical simulators, requiring the use of simulation-based approaches to statistical inference. I will talk about our recent work on developing simulation based inference-methods using flexible density estimators parameterised with neural networks, our efforts on benchmarking these approaches, and applications to modelling problems in neuroscience.

Remember you need to register beforehand to receive the access code!

## ABC on brain networks

Posted in Books, pictures, Statistics, University life with tags ABC, ABC model choice, Approximate Bayesian computation, Bayes factor, Bayesian model choice, brain model, brains, copula, evidence, network, Parkinson, research gate, summary statistics, tolerance on April 16, 2021 by xi'an**R**esearch Gate sent me an automated email pointing out a recent paper citing some of our ABC papers. The paper is written by Timothy West et al., neuroscientists in the UK, comparing models of Parkinsonian circuit dynamics. Using SMC-ABC. One novelty is the update of the tolerance by a fixed difference, unless the acceptance rate is too low, in which case the tolerance is reinitialised to a starting value.

“(…) the proposal density P(θ|D⁰) is formed from the accepted parameters sets. We use a density approximation to the marginals and a copula for the joint (…) [i.e.] a nonparametric estimation of the marginal densities overeach parameter [and] the t-copula(…) Data are transformed to the copula scale (unit-square) using the kernel density estimator of the cumulative distribution function of each parameter and then transformed to the joint space with the t-copula.”

The construct of the proposal is quite involved, as described in the above quote. The model choice approach is standard (à la Grelaud et al.) but uses the median distance as a tolerance.

“(…) test whether the ABC estimator will: a) yield parameter estimates that are unique to the data fromwhich they have been optimized; and b) yield consistent estimation of parameters across multiple instances (…) test the face validity of the model comparison framework (…) [and] demonstrate the scalability of the optimization and model comparison framework.”

The paper runs a fairly extensive test of the above features, concluding that “the ABC optimized posteriors are consistent across multiple initializations and that the output is determined by differences in the underlying model generating the given data.” Concerning model comparison, the authors mix the ABC Bayes factor with a post-hoc analysis of divergence to discriminate against overfitting. And mention the potential impact of the summary statistics in the conclusion section, albeit briefly, and the remark that the statistics were “sufficient to recover known parameters” is not supporting their use for model comparison. The additional criticism of sampling strategies for approximating Bayes factors is somewhat irrelevant, the main issue with ABC model choice being a change of magnitude in the evidence.

“ABC has established itself as a key tool for parameter estimation in systems biology (…) but is yet to see wide adoption in systems neuroscience. It is known that ABC will not perform well under certain conditions (Sunnåker et al., 2013). Specifically, it has been shown that the

simplest form of ABC algorithm based upon an rejection-sampling approach is inefficient in the case where the prior densities lie far from the true posterior (…) This motivates the use of neurobiologically grounded models over phenomenological models where often the ranges of potential parameter values are unknown.”

## the new DIYABC-RF

Posted in Books, pictures, R, Statistics, Wines with tags ABC, admixture, Approximate Bayesian computation, Bayesian model choice, demographic history, DIYABC, effective population size, genetic polymorphisms, github, likelihood-free inference, most recent common ancestor, R, R shiny, random forests, software, supervised machine learning, Université de Montpellier on April 15, 2021 by xi'an**M**y friends and co-authors from Montpellier have released last month the third version of the DIYABC software, DIYABC-RF, which includes and promotes the use of random forests for parameter inference and model selection, in connection with Louis Raynal’s thesis. Intended as the earlier versions of DIYABC for population genetic applications. Bienvenue!!!

The software DIYABC Random Forest (hereafter DIYABC-RF) v1.0 is composed of three parts: the dataset simulator, the Random Forest inference engine and the graphical user interface. The whole is packaged as a standalone and user-friendly graphical application named DIYABC-RF GUI and available at https://diyabc.github.io. The different developer and user manuals for each component of the software are available on the same website. DIYABC-RF is a multithreaded software on three operating systems: GNU/Linux, Microsoft Windows and MacOS. One can use the program can be used through a modern and user-friendly graphical interface designed as an R shiny application (Chang et al. 2019). For a fluid and simplified user experience, this interface is available through a standalone application, which does not require installing R or any dependencies and hence can be used independently. The application is also implemented in an R package providing a standard shiny web application (with the same graphical interface) that can be run locally as any shiny application, or hosted as a web service to provide a DIYABC-RF server for multiple users.