“We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations.” (p.1) Philipp Hennig, Michael Osborne and Mark Girolami (Warwick) posted on arXiv a paper to appear in Proceedings A of the Royal Statistical Society that relates […]

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## probabilistic numerics and uncertainty in computations

June 10, 2015## probabilistic numerics

April 27, 2015I attended an highly unusual workshop while in Warwick last week. Unusual for me, obviously. It was about probabilistic numerics, i.e., the use of probabilistic or stochastic arguments in the numerical resolution of (possibly) deterministic problems. The notion in this approach is fairly Bayesian in that it makes use to prior information or belief about […]

## Bayesian probabilistic numerical methods

December 5, 2019“…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 […]

## séminaire P de S

February 18, 2020As I was in Paris and free for the occasion (!), I attended the Paris Statistics seminar this afternoon, in the Latin Quarter. With a first talk by Kweku Abraham on Bayesian inverse problems set a prior on the quantity of interest, γ, rather than its transform G(γ), observed with noise. Always perturbed by the […]

## Bayesian conjugate gradients [open for discussion]

June 25, 2019When fishing for an illustration for this post on Google, I came upon this Bayesian methods for hackers cover, a book about which I have no clue whatsoever (!) but that mentions probabilistic programming. Which serves as a perfect (?!) introduction to the call for discussion in Bayesian Analysis of the incoming Bayesian conjugate gradient […]