442 citations · 3.3k across the 135 of their papers we have counts for
17 papers · 1 filter
Causal Falsification of Digital Twins
Rob Cornish, Muhammad Faaiz Taufiq, Arnaud Doucet +1
Digital twins are simulation-based models designed to predict how a real-world process will evolve in response to interventions. This modelling paradigm holds substantial promise i…
A Particle Method for Solving Fredholm Equations of the First Kind
Francesca R Crucinio, Arnaud Doucet, Adam M Johansen
Fredholm integral equations of the first kind are the prototypical example of ill-posed linear inverse problems. They model, among other things, reconstruction of distorted noisy o…
Scalable Monte Carlo inference for state-space models
Sinan Yıldırım, Christophe Andrieu, Arnaud Doucet
We present an original simulation-based method to estimate likelihood ratios efficiently for general state-space models. Our method relies on a novel use of the conditional Sequent…
Piecewise-Deterministic Markov Chain Monte Carlo
Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis +1
A novel class of non-reversible Markov chain Monte Carlo schemes relying on continuous-time piecewise-deterministic Markov Processes has recently emerged. In these algorithms, the…
Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
Joris Bierkens, Alexandre Bouchard-Côté, Arnaud Doucet +5
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration. We show how th…
Pseudo-Marginal Hamiltonian Monte Carlo
Johan Alenlöv, Arnaud Doucet, Fredrik Lindsten
Bayesian inference in the presence of an intractable likelihood function is computationally challenging. When following a Markov chain Monte Carlo (MCMC) approach to approximate th…