
Gaussian Processes on Hypergraphs
We derive a Matern Gaussian process (GP) on the vertices of a hypergraph...
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Stochastic Gradient MCMC with MultiArmed Bandit Tuning
Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class...
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Robust Bayesian Nonparametric Variable Selection for Linear Regression
Spikeandslab and horseshoe regression are arguably the most popular Ba...
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A Probabilistic Assessment of the COVID19 Lockdown on Air Quality in the UK
In March 2020 the United Kingdom (UK) entered a nationwide lockdown peri...
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Stein Variational Gaussian Processes
We show how to use Stein variational gradient descent (SVGD) to carry ou...
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Latent Space Representations of Hypergraphs
The increasing prevalence of relational data describing interactions amo...
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Stochastic gradient Markov chain Monte Carlo
Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the...
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Stochastic Gradient MCMC for Nonlinear State Space Models
State space models (SSMs) provide a flexible framework for modeling comp...
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GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language
Gaussian processes are a class of flexible nonparametric Bayesian tools ...
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LargeScale Stochastic Sampling from the Probability Simplex
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popul...
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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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Pseudoextended Markov chain Monte Carlo
Sampling from the posterior distribution using Markov chain Monte Carlo ...
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Control Variates for Stochastic Gradient MCMC
It is well known that Markov chain Monte Carlo (MCMC) methods scale poor...
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Merging MCMC Subposteriors through GaussianProcess Approximations
Markov chain Monte Carlo (MCMC) algorithms have become powerful tools fo...
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Particle Metropolisadjusted Langevin algorithms
This paper proposes a new sampling scheme based on Langevin dynamics tha...
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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...
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Christopher Nemeth
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