
Optimal Scaling of MCMC Beyond Metropolis
The problem of optimally scaling the proposal distribution in a Markov c...
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Speed Up ZigZag
ZigZag is Piecewise Deterministic Markov Process, efficiently used for ...
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RaoBlackwellization in the MCMC era
RaoBlackwellization is a notion often occurring in the MCMC literature,...
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Skew Brownian Motion and Complexity of the ALPS Algorithm
Simulated tempering is a popular method of allowing MCMC algorithms to m...
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The computational cost of blocking for sampling discretely observed diffusions
Many approaches for conducting Bayesian inference on discretely observed...
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An epidemic model for an evolving pathogen with straindependent immunity
Between pandemics, the influenza virus exhibits periods of incremental e...
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Exact Bayesian inference for diffusion driven Cox processes
In this paper we present a novel methodology to perform Bayesian inferen...
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Efficient Bernoulli factory MCMC for intractable likelihoods
Acceptreject based Markov chain Monte Carlo (MCMC) algorithms have trad...
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Regenerationenriched Markov processes with application to Monte Carlo
We study a class of Markov processes comprising local dynamics governed ...
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Optimal Scaling of Metropolis Algorithms on General Target Distributions
The main limitation of the existing optimal scaling results for Metropol...
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Simulating bridges using confluent diffusions
Diffusions are a fundamental class of models in many fields, including f...
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Optimal Temperature Spacing for Regionally Weightpreserving Tempering
Parallel tempering is popular method for allowing MCMC algorithms to pro...
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Accelerating Parallel Tempering: Quantile Tempering Algorithm (QuanTA)
Using MCMC to sample from a target distribution, π(x) on a ddimensional...
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An Approximation Scheme for Quasistationary Distributions of Killed Diffusions
In this paper we study the asymptotic behavior of the normalized weighte...
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WeightPreserving Simulated Tempering
Simulated tempering is popular method of allowing MCMC algorithms to mov...
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Scalable inference for crossed random effects models
We analyze the complexity of Gibbs samplers for inference in crossed ran...
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Continioustime Importance Sampling: Monte Carlo Methods which Avoid Timediscretisation Error
In this paper we develop a continuoustime sequential importance samplin...
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Barker's algorithm for Bayesian inference with intractable likelihoods
In this expository paper we abstract and describe a simple MCMC scheme f...
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Gareth O. Roberts
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