**T**he programme for our [AG:DC] 12-14 March satellite of BayesComp 2023 in Levi, Finland, is now on-line. (There will be a gondola shuttle running from town to hotel for all sessions.)

## Archive for the Statistics Category

## BayesComp Satellite [AG:DC] program

Posted in Statistics with tags ABC, AG:DC workshop, approximate MCMC, BayesComp 2023, Finland, Levi, likelihood-free methods, MCMSki on February 1, 2023 by xi'an## snapshot from Martinique² [jatp]

Posted in Statistics with tags Atlantic ocean, Îlet Sainte-Marie, Caribean sea, jatp, Le Tombolo, Martinique, Montagne Pelée, Petite Anse, Sainte-Marie, tombolo on January 31, 2023 by xi'an## ABC with path signatures [One World ABC seminar, 2/2/23]

Posted in Books, pictures, Running, Statistics, Travel, University life with tags ABC, Approximate Bayesian computation, Bayesian time series analysis, Coventry, likelihood-free inference, One World ABC Seminar, University of Warwick, webinar on January 29, 2023 by xi'an

The next One World ABC seminar is by Joel Dyer (Oxford) at 1:30pm (UK time) on 02 February.

**Title**: Approximate Bayesian Computation with Path Signatures

**Abstract**: Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable. Approximate Bayesian computation (ABC) generates likelihood-free posterior samples by comparing simulated and observed data through some distance measure, but existing approaches are often poorly suited to time series simulators, for example due to an independent and identically distributed data assumption. In this talk, we will discuss our work on the use of path signatures in ABC as a means to handling the sequential nature of time series data of different kinds. We will begin by discussing popular approaches to ABC and how they may be extended to time series simulators. We will then introduce path signatures, and discuss how signatures naturally lead to two instances of ABC for time series simulators. Finally, we will demonstrate that the resulting signature-based ABC procedures can produce competitive Bayesian parameter inference for simulators generating univariate, multivariate, irregularly spaced, and even non-Euclidean sequences.

**Reference**: J. Dyer, P. Cannon, S. M Schmon (2022). Approximate Bayesian Computation with Path Signatures. arXiv preprint 2106.12555

## Bayesian thinking for toddler & Bayesian probabilities for babies [book reviews]

Posted in Statistics with tags baby book, basic probability, Bayesian Thinking for Toddlers, book review, CHANCE, dinosaur, Harold Jeffreys, JASP, Ockham's razor, thesis defence, University of Amsterdam on January 27, 2023 by xi'an**M**y friend E.-J. Wagenmakers sent me a copy of *Bayesian Thinking for Toddlers*, “a must-have for any toddler with even a passing interest in Ockham’s razor and the prequential principle.” E.-J. wrote the story and Viktor Beekman (of thesis’ cover fame!) drew the illustrations. The book can be read for free on https://psyarxiv.com/w5vbp/, but not purchased as publishers were not interested and self-publishing was not available at a high enough quality level. Hence, in the end, 200 copies were made as JASP material, with me being the happy owner of one of these. The story follows two young girls competing for dinosaur expertise, and being rewarded by cookies, in proportion to the probability of providing the correct answer to two dinosaur questions. Toddlers may get less enthusiastic than grown-ups about the message, but they will love the drawings (and the questions if they are into dinosaurs).

This reminded me of the Bayesian probabilities for babies book, by Chris Ferrie, which details the computation of the probability that a cookie contains candy when the first bite holds none. It is more genuinely intended for young kids, in shape and design, as can be checked on a YouTube video, with an hypothetical population of cookies (with and without candy) being the proxy for the prior distribution. I hope no baby will be traumatised from being exposed too early to the notions of prior and posterior. Only data can tell, twenty years from now, if the book induced a spike or a collapse in the proportion of Bayesian statisticians!

*[Disclaimer about potential self-plagiarism: this post or an edited version will potentially appear in my Books Review section in CHANCE.*