
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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