Adam M. Johansen | DeepAI

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Properties of Marginal Sequential Monte Carlo Methods

We provide a framework which admits a number of “marginal” sequential Mo...

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∙ 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...

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∙ 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...

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∙ 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...

0 Francesca R Crucinio, et al. ∙

∙ 04/27/2022

Scalable particle-based alternatives to EM

Building on (Neal and Hinton, 1998), where the problem tackled by EM is ...

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∙ 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...

0 Juan Kuntz, et al. ∙

∙ 10/14/2021

Divide-and-Conquer Monte Carlo Fusion

Combining several (sample approximations of) distributions, which we ter...

0 Ryan S. Y. Chan, et al. ∙

∙ 10/11/2021

Weak Convergence of Non-neutral Genealogies to Kingman's Coalescent

Interacting particle populations undergoing repeated mutation and fitnes...

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∙ 09/17/2021

The Node-wise Pseudo-marginal Method

Motivated by problems from neuroimaging in which existing approaches mak...

0 Denishrouf Thesingarajah, et al. ∙

∙ 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...

0 Juan Kuntz, et al. ∙

∙ 02/16/2021

Unbiased simulation of rare events in continuous time

For rare events described in terms of Markov processes, truly unbiased e...

0 James Hodgson, et al. ∙

∙ 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...

0 Francesca R Crucinio, et al. ∙

∙ 06/30/2020

Simple conditions for convergence of sequential Monte Carlo genealogies with applications

Sequential Monte Carlo algorithms are popular methods for approximating ...

0 Suzie Brown, et al. ∙

∙ 02/01/2019

Limit theorems for cloning algorithms

Large deviations for additive path functionals of stochastic processes h...

0 Letizia Angeli, et al. ∙

∙ 07/24/2018

Global consensus Monte Carlo

For Bayesian inference with large data sets, it is often convenient or n...

0 Lewis J. Rendell, et al. ∙

∙ 07/03/2018

Limit theorems for sequential MCMC methods

Sequential Monte Carlo (SMC) methods, also known as particle filters, co...

0 Axel Finke, et al. ∙

∙ 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...

0 Jere Koskela, et al. ∙

∙ 04/01/2015

Bayesian model comparison with un-normalised likelihoods

Models for which the likelihood function can be evaluated only up to a p...

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