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07/27/2023
Insufficient Gibbs Sampling
In some applied scenarios, the availability of complete data is restrict...
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10/18/2022
Sampling using Adaptive Regenerative Processes
Enriching Brownian Motion with regenerations from a fixed regeneration d...
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08/01/2022
Computing Bayes: From Then 'Til Now'
This paper takes the reader on a journey through the history of Bayesian...
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07/17/2022
The Importance Markov Chain
The Importance Markov chain is a new algorithm bridging the gap between ...
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06/14/2022
50 shades of Bayesian testing of hypotheses
Hypothesis testing and model choice are quintessential questions for sta...
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05/11/2022
Evidence estimation in finite and infinite mixture models and applications
Estimating the model evidence - or mariginal likelihood of the data - is...
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12/20/2021
Approximating Bayes in the 21st Century
The 21st century has seen an enormous growth in the development and use ...
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10/28/2021
Living on the Edge: An Unified Approach to Antithetic Sampling
We identify recurrent ingredients in the antithetic sampling literature ...
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01/04/2021
Rao-Blackwellization in the MCMC era
Rao-Blackwellization is a notion often occurring in the MCMC literature,...
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04/14/2020
Computing Bayes: Bayesian Computation from 1763 to the 21st Century
The Bayesian statistical paradigm uses the language of probability to ex...
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02/11/2020
Generalized Poisson Difference Autoregressive Processes
This paper introduces a new stochastic process with values in the set Z ...
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01/17/2020
Markov Chain Monte Carlo Methods, a survey with some frequent misunderstandings
In this chapter, we review some of the most standard MCMC tools used in ...
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11/21/2019
Parallelising MCMC via Random Forests
For Bayesian computation in big data contexts, the divide-and-conquer MC...
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05/31/2019
Component-wise approximate Bayesian computation via Gibbs-like steps
Approximate Bayesian computation methods are useful for generative model...
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05/09/2019
Approximate Bayesian computation with the Wasserstein distance
A growing number of generative statistical models do not permit the nume...
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12/24/2018
Model Selection for Mixture Models - Perspectives and Strategies
Determining the number G of components in a finite mixture distribution ...
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12/18/2018
Computational Solutions for Bayesian Inference in Mixture Models
This chapter surveys the most standard Monte Carlo methods available for...
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10/10/2018
Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale
Hamiltonian Monte Carlo samplers have become standard algorithms for MCM...
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09/11/2018
Rethinking the Effective Sample Size
The effective sample size (ESS) is widely used in sample-based simulatio...
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09/10/2018
The Coordinate Sampler: A Non-Reversible Gibbs-like MCMC Sampler
In this article, we derive a novel non-reversible, continuous-time Marko...
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04/08/2018
Accelerating MCMC Algorithms
Markov chain Monte Carlo algorithms are used to simulate from complex st...
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05/18/2016
ABC random forests for Bayesian parameter inference
This preprint has been reviewed and recommended by Peer Community In Evo...
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03/26/2015
Likelihood-free Model Choice
This document is an invited chapter covering the specificities of ABC mo...
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06/24/2014