137 citations · 432 across the 44 of their papers we have counts for
46 papers
Solving the Poisson equation using coupled Markov chains
Randal Douc, Pierre E. Jacob, Anthony Lee +1
This article shows how coupled Markov chains that meet exactly after a random number of iterations can be used to generate unbiased estimators of the solutions of the Poisson equat…
Asymptotics of cut distributions and robust modular inference using Posterior Bootstrap
Emilia Pompe, Mikołaj J. Kasprzak, Pierre E. Jacob
Bayesian inference provides a framework to combine various model components with shared parameters, allowing joint uncertainty estimation and the use of all available data sources.…
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections
Kimia Nadjahi, Alain Durmus, Pierre E. Jacob +2
The Sliced-Wasserstein distance (SW) is being increasingly used in machine learning applications as an alternative to the Wasserstein distance and offers significant computational…
Sequential Monte Carlo algorithms for agent-based models of disease transmission
Nianqiao Ju, Jeremy Heng, Pierre E. Jacob
Agent-based models of disease transmission involve stochastic rules that specify how a number of individuals would infect one another, recover or be removed from the population. Co…
A simple Markov chain for independent Bernoulli variables conditioned on their sum
Jeremy Heng, Pierre E. Jacob, Nianqiao Ju
We consider a vector of independent binary variables, each with a different probability of success. The distribution of the vector conditional on its sum is known as the condit…
Coupling-based convergence assessment of some Gibbs samplers for high-dimensional Bayesian regression with shrinkage priors
Niloy Biswas, Anirban Bhattacharya, Pierre E. Jacob +1
We consider Markov chain Monte Carlo (MCMC) algorithms for Bayesian high-dimensional regression with continuous shrinkage priors. A common challenge with these algorithms is the ch…