11 citations · 30 across the 21 of their papers we have counts for
6 papers · 1 filter
Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design
Sahel Iqbal, Hany Abdulsamad, Sara Pérez-Vieites +2
This paper introduces the Inside-Out Nested Particle Filter (IO-NPF), a novel, fully recursive, algorithm for amortized sequential Bayesian experimental design in the non-exchangea…
Conditioning diffusion models by explicit forward-backward bridging
Adrien Corenflos, Zheng Zhao, Simo Särkkä +2
Given an unconditional diffusion model targeting a joint model , using it to perform conditional simulation is still largely an open question and is typicall…
BlackJAX: Composable Bayesian inference in JAX
Alberto Cabezas, Adrien Corenflos, Junpeng Lao +19
BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by tak…
Nesting Particle Filters for Experimental Design in Dynamical Systems
Sahel Iqbal, Adrien Corenflos, Simo Särkkä +1
In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside…
Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models
Adrien Corenflos, Axel Finke
State-of-the-art methods for Bayesian inference in state-space models are (a) conditional sequential Monte Carlo (CSMC) algorithms; (b) sophisticated 'classical' MCMC algorithms li…
Modelling pathwise uncertainty of Stochastic Differential Equations samplers via Probabilistic Numerics
Yvann Le Fay, Simo Särkkä, Adrien Corenflos
Probabilistic ordinary differential equation (ODE) solvers have been introduced over the past decade as uncertainty-aware numerical integrators. They typically proceed by assuming…