Christopher Nemeth | DeepAI

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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...

0 Louis Sharrock, et al. ∙

∙ 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...

0 Louis Sharrock, et al. ∙

∙ 01/26/2023

Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates

In recent years, particle-based variational inference (ParVI) methods su...

0 Louis Sharrock, et al. ∙

∙ 10/28/2022

Preferential Subsampling for Stochastic Gradient Langevin Dynamics

Stochastic gradient MCMC (SGMCMC) offers a scalable alternative to tradi...

0 Srshti Putcha, et al. ∙

∙ 10/19/2022

Transport Elliptical Slice Sampling

We introduce a new framework for efficient sampling from complex probabi...

0 Alberto Cabezas, et al. ∙

∙ 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...

0 Callum Vyner, et al. ∙

∙ 06/20/2022

Modelling Populations of Interaction Networks via Distance Metrics

Network data arises through observation of relational information betwee...

0 George Bolt, et al. ∙

∙ 06/17/2022

Distances for Comparing Multisets and Sequences

Measuring the distance between data points is fundamental to many statis...

0 George Bolt, et al. ∙

∙ 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...

0 Oluwole Oyebamiji, et al. ∙

∙ 12/19/2021

Sequential Estimation of Temporally Evolving Latent Space Network Models

In this article we focus on dynamic network data which describe interact...

0 Kathryn Turnbull, et al. ∙

∙ 06/03/2021

Gaussian Processes on Hypergraphs

We derive a Matern Gaussian process (GP) on the vertices of a hypergraph...

15 Thomas Pinder, et al. ∙

∙ 05/27/2021

Stochastic Gradient MCMC with Multi-Armed Bandit Tuning

Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class...

0 Jeremie Coullon, et al. ∙

∙ 05/23/2021

Robust Bayesian Nonparametric Variable Selection for Linear Regression

Spike-and-slab and horseshoe regression are arguably the most popular Ba...

0 Alberto Cabezas, et al. ∙

∙ 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...

0 Thomas Pinder, et al. ∙

∙ 09/25/2020

Stein Variational Gaussian Processes

We show how to use Stein variational gradient descent (SVGD) to carry ou...

0 Thomas Pinder, et al. ∙

∙ 09/01/2019

Latent Space Representations of Hypergraphs

The increasing prevalence of relational data describing interactions amo...

0 Kathryn Turnbull, et al. ∙

∙ 07/16/2019

Stochastic gradient Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the...

4 Christopher Nemeth, et al. ∙

∙ 01/29/2019

Stochastic Gradient MCMC for Nonlinear State Space Models

State space models (SSMs) provide a flexible framework for modeling comp...

12 Christopher Aicher, et al. ∙

∙ 12/21/2018

GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language

Gaussian processes are a class of flexible nonparametric Bayesian tools ...

0 Jamie Fairbrother, et al. ∙

∙ 06/19/2018

Large-Scale Stochastic Sampling from the Probability Simplex

Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popul...

0 Jack Baker, et al. ∙

∙ 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...

0 Jack Baker, et al. ∙

∙ 08/17/2017

Pseudo-extended Markov chain Monte Carlo

Sampling from the posterior distribution using Markov chain Monte Carlo ...

0 Christopher Nemeth, et al. ∙

∙ 06/16/2017

Control Variates for Stochastic Gradient MCMC

It is well known that Markov chain Monte Carlo (MCMC) methods scale poor...

0 Jack Baker, et al. ∙

∙ 05/27/2016

Merging MCMC Subposteriors through Gaussian-Process Approximations

Markov chain Monte Carlo (MCMC) algorithms have become powerful tools fo...

0 Christopher Nemeth, et al. ∙

∙ 12/23/2014

Particle Metropolis-adjusted Langevin algorithms

This paper proposes a new sampling scheme based on Langevin dynamics tha...

0 Christopher Nemeth, et al. ∙

∙ 06/04/2013

Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost

Poyiadjis et al. (2011) show how particle methods can be used to estimat...

0 Christopher Nemeth, et al. ∙

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