research
∙
03/06/2023
Properties of Marginal Sequential Monte Carlo Methods
We provide a framework which admits a number of “marginal” sequential Mo...
share
research
∙
12/23/2022
Cost free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao-Blackwellized SMC Samplers
In Bayesian inverse problems, one aims at characterizing the posterior d...
research
∙
11/25/2022
A divide and conquer sequential Monte Carlo approach to high dimensional filtering
We propose a divide-and-conquer approach to filtering which decomposes t...
research
∙
09/16/2022
Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows
Solving Fredholm equations of the first kind is crucial in many areas of...
research
∙
04/27/2022
Scalable particle-based alternatives to EM
Building on (Neal and Hinton, 1998), where the problem tackled by EM is ...
research
∙
10/29/2021
The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems
We revisit the divide-and-conquer sequential Monte Carlo (DaC-SMC) algor...
research
∙
10/14/2021
Divide-and-Conquer Monte Carlo Fusion
Combining several (sample approximations of) distributions, which we ter...
research
∙
10/11/2021
Weak Convergence of Non-neutral Genealogies to Kingman's Coalescent
Interacting particle populations undergoing repeated mutation and fitnes...
research
∙
09/17/2021
The Node-wise Pseudo-marginal Method
Motivated by problems from neuroimaging in which existing approaches mak...
research
∙
02/23/2021
Product-form estimators: exploiting independence to scale up Monte Carlo
We introduce a class of Monte Carlo estimators for product-form target d...
research
∙
02/16/2021
Unbiased simulation of rare events in continuous time
For rare events described in terms of Markov processes, truly unbiased e...
research
∙
09/21/2020
A Particle Method for Solving Fredholm Equations of the First Kind
Fredholm integral equations of the first kind are the prototypical examp...
research
∙
06/30/2020
Simple conditions for convergence of sequential Monte Carlo genealogies with applications
Sequential Monte Carlo algorithms are popular methods for approximating ...
research
∙
02/01/2019
Limit theorems for cloning algorithms
Large deviations for additive path functionals of stochastic processes h...
research
∙
07/24/2018
Global consensus Monte Carlo
For Bayesian inference with large data sets, it is often convenient or n...
research
∙
07/03/2018
Limit theorems for sequential MCMC methods
Sequential Monte Carlo (SMC) methods, also known as particle filters, co...
research
∙
04/05/2018
Asymptotic genealogies of interacting particle systems with an application to sequential Monte Carlo
We consider weighted particle systems of fixed size, in which a new gene...
research
∙
04/01/2015
