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05/24/2023
Learning Rate Free Bayesian Inference in Constrained Domains
We introduce a suite of new particle-based algorithms for sampling on co...
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05/24/2023
CoinEM: Tuning-Free Particle-Based Variational Inference for Latent Variable Models
We introduce two new particle-based algorithms for learning latent varia...
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01/26/2023
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
In recent years, particle-based variational inference (ParVI) methods su...
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10/28/2022
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
Stochastic gradient MCMC (SGMCMC) offers a scalable alternative to tradi...
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10/19/2022
Transport Elliptical Slice Sampling
We introduce a new framework for efficient sampling from complex probabi...
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08/08/2022
SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy
Divide-and-conquer strategies for Monte Carlo algorithms are an increasi...
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06/20/2022
Modelling Populations of Interaction Networks via Distance Metrics
Network data arises through observation of relational information betwee...
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06/17/2022
Distances for Comparing Multisets and Sequences
Measuring the distance between data points is fundamental to many statis...
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01/24/2022
Multivariate sensitivity analysis for a large-scale climate impact and adaptation model
We develop a new efficient methodology for Bayesian global sensitivity a...
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12/19/2021
Sequential Estimation of Temporally Evolving Latent Space Network Models
In this article we focus on dynamic network data which describe interact...
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06/03/2021
Gaussian Processes on Hypergraphs
We derive a Matern Gaussian process (GP) on the vertices of a hypergraph...
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05/27/2021
Stochastic Gradient MCMC with Multi-Armed Bandit Tuning
Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class...
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05/23/2021
Robust Bayesian Nonparametric Variable Selection for Linear Regression
Spike-and-slab and horseshoe regression are arguably the most popular Ba...
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04/22/2021
A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK
In March 2020 the United Kingdom (UK) entered a nationwide lockdown peri...
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09/25/2020
Stein Variational Gaussian Processes
We show how to use Stein variational gradient descent (SVGD) to carry ou...
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09/01/2019
Latent Space Representations of Hypergraphs
The increasing prevalence of relational data describing interactions amo...
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07/16/2019
Stochastic gradient Markov chain Monte Carlo
Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the...
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01/29/2019
Stochastic Gradient MCMC for Nonlinear State Space Models
State space models (SSMs) provide a flexible framework for modeling comp...
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12/21/2018
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language
Gaussian processes are a class of flexible nonparametric Bayesian tools ...
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06/19/2018
Large-Scale Stochastic Sampling from the Probability Simplex
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popul...
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10/02/2017
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
This paper introduces the R package sgmcmc; which can be used for Bayesi...
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08/17/2017
Pseudo-extended Markov chain Monte Carlo
Sampling from the posterior distribution using Markov chain Monte Carlo ...
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06/16/2017
Control Variates for Stochastic Gradient MCMC
It is well known that Markov chain Monte Carlo (MCMC) methods scale poor...
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05/27/2016
Merging MCMC Subposteriors through Gaussian-Process Approximations
Markov chain Monte Carlo (MCMC) algorithms have become powerful tools fo...
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12/23/2014
Particle Metropolis-adjusted Langevin algorithms
This paper proposes a new sampling scheme based on Langevin dynamics tha...
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06/04/2013
