Search Results for author:
Found 10 papers, 9 papers with code
BlackJAX: Composable Bayesian inference in JAX
BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation.
Nesting Particle Filters for Experimental Design in Dynamical Systems
In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization.
Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models
In experiments, for both highly and weakly informative prior dynamics, our methods substantially improve upon both CSMC and sophisticated 'classical' MCMC approaches.
Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing
Stochastic optimal control of dynamical systems is a crucial challenge in sequential decision-making.
Parallel-in-Time Probabilistic Numerical ODE Solvers
Probabilistic numerical solvers for ordinary differential equations (ODEs) treat the numerical simulation of dynamical systems as problems of Bayesian state estimation.
Variational Gaussian filtering via Wasserstein gradient flows
We present a novel approach to approximate Gaussian and mixture-of-Gaussians filtering.
Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems
We introduce two new classes of exact Markov chain Monte Carlo (MCMC) samplers for inference in latent dynamical models.
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
We propose dSMC (de-Sequentialized Monte Carlo), a new particle smoother that is able to process $T$ observations in $\mathcal{O}(\log T)$ time on parallel architecture.
Temporal Gaussian Process Regression in Logarithmic Time
The aim of this article is to present a novel parallelization method for temporal Gaussian process (GP) regression problems.
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models.
