**T**he première of the ABC World Seminar last Thursday was most successful! It took place at the scheduled time, with no technical interruption and allowed 130⁺ participants from most of the World [sorry, West Coast friends!] to listen to the first speaker, Dennis Prangle, presenting normalising flows and distilled importance sampling. And to answer questions. As I had already commented on the earlier version of his paper, I will not reproduce them here. In short, I remain uncertain, albeit not skeptical, about the notions of normalising flows and variational encoders for estimating densities, when perceived as a non-parametric estimator due to the large number of parameters it involves and wonder at the availability of convergence rates. Incidentally, I had forgotten at the remarkable link between KL distance & importance sampling variability. Adding to the to-read list Müller et al. (2018) on neural importance sampling.

## Archive for Bayesian nonparametrics

## ABC webinar, first!

Posted in Books, pictures, Statistics, University life with tags ABC, adaptive importance sampling, Approximate Bayesian computation, Bayesian neural networks, Bayesian nonparametrics, confinement, coronavirus epidemics, COVID-19, Kullback-Leibler divergence, normalising flow, webinar on April 13, 2020 by xi'an## Judith’s colloquium at Warwick

Posted in Statistics with tags Bayesian inference, Bayesian nonparametrics, Bayesian tests of hypotheses, colloquium, Hawkes processes, Judith Rousseau, seminar, University of Oxford, University of Warwick on February 21, 2020 by xi'an## BayesComp 2020 at a glance

Posted in Statistics, Travel, University life with tags ABC, BayesComp 2020, Bayesian computation, Bayesian nonparametrics, conference, Gainesville, Gaussian processes, HMC, ISBA, MCMC, non-reversible diffusion, poster session, reversible Markov chain, simulation, University of Florida, USA, Wasserstein distance on December 18, 2019 by xi'an## Bayesian probabilistic numerical methods

Posted in Books, pictures, Statistics, University life with tags ANOVA, Bayesian nonparametrics, probabilistic numerics, SIAM, Siam Review, Society for Industrial and Applied Mathematics, University of Warwick on December 5, 2019 by xi'an

“…in isolation, the error of a numerical method can often be studied and understood, but when composed into a pipeline the resulting error structure maybe non-trivial and its analysis becomes more difficult. The real power of probabilistic numerics lies in its application to pipelines of numerical methods, where the probabilistic formulation permits analysis of variance (ANOVA) to understand the contribution of each discretisation to the overall numerical error.”

**J**on Cockayne (Warwick), Chris Oates (formerly Warwick), T.J. Sullivan, and Mark Girolami (formerly Warwick) got their survey on Bayesian probabilistic numerical methods in the SIAM (Society for Industrial and Applied Mathematics) Review, which is quite a feat given the non-statistical flavour of the journal (although Art Owen is now one of the editors of the review). As already reported in some posts on the ‘Og, the concept relies on the construction of a prior measure over a set of potential solutions, and numerical methods are assessed against the associated posterior measure. Not only is this framework more compelling in a conceptual sense, but it also leads to novel probabilistic numerical methods managing to solve quite challenging numerical tasks. Congrats to the authors!