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06/09/2023
Bayesian inference for high-dimensional discrete-time epidemic models: spatial dynamics of the UK COVID-19 outbreak
In the event of a disease outbreak emergency, such as COVID-19, the abil...
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05/01/2023
Scaling of Piecewise Deterministic Monte Carlo for Anisotropic Targets
Piecewise deterministic Markov processes (PDMPs) are a type of continuou...
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01/06/2023
Optimal Scaling Results for a Wide Class of Proximal MALA Algorithms
We consider a recently proposed class of MCMC methods which uses proximi...
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12/08/2022
The Lifebelt Particle Filter for robust estimation from low-valued count data
Particle filtering methods are well developed for continuous state-space...
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10/18/2022
Sampling using Adaptive Regenerative Processes
Enriching Brownian Motion with regenerations from a fixed regeneration d...
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06/22/2022
Automatic Zig-Zag sampling in practice
Novel Monte Carlo methods to generate samples from a target distribution...
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05/24/2022
Stereographic Markov Chain Monte Carlo
High dimensional distributions, especially those with heavy tails, are n...
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05/12/2022
Football Group Draw Probabilities and Corrections
This paper considers the challenge of designing football group draw mech...
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12/24/2021
Annealed Leap-Point Sampler for Multimodal Target Distributions
In Bayesian statistics, exploring multimodal posterior distribution pose...
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10/14/2021
Divide-and-Conquer Monte Carlo Fusion
Combining several (sample approximations of) distributions, which we ter...
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06/10/2021
Bayesian semi-parametric inference for diffusion processes using splines
We introduce a semi-parametric method to simultaneously infer both the d...
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05/12/2021
Dimension-free Mixing for High-dimensional Bayesian Variable Selection
Yang et al. (2016) proved that the symmetric random walk Metropolis–Hast...
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04/05/2021
Optimal Scaling of MCMC Beyond Metropolis
The problem of optimally scaling the proposal distribution in a Markov c...
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03/30/2021
Speed Up Zig-Zag
Zig-Zag is Piecewise Deterministic Markov Process, efficiently used for ...
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01/04/2021
Rao-Blackwellization in the MCMC era
Rao-Blackwellization is a notion often occurring in the MCMC literature,...
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09/25/2020
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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09/22/2020
The computational cost of blocking for sampling discretely observed diffusions
Many approaches for conducting Bayesian inference on discretely observed...
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08/17/2020
An epidemic model for an evolving pathogen with strain-dependent immunity
Between pandemics, the influenza virus exhibits periods of incremental e...
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07/11/2020
Exact Bayesian inference for diffusion driven Cox processes
In this paper we present a novel methodology to perform Bayesian inferen...
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04/16/2020
Efficient Bernoulli factory MCMC for intractable likelihoods
Accept-reject based Markov chain Monte Carlo (MCMC) algorithms have trad...
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10/11/2019
Regeneration-enriched Markov processes with application to Monte Carlo
We study a class of Markov processes comprising local dynamics governed ...
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04/27/2019
Optimal Scaling of Metropolis Algorithms on General Target Distributions
The main limitation of the existing optimal scaling results for Metropol...
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03/25/2019
Simulating bridges using confluent diffusions
Diffusions are a fundamental class of models in many fields, including f...
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10/13/2018
Optimal Temperature Spacing for Regionally Weight-preserving Tempering
Parallel tempering is popular method for allowing MCMC algorithms to pro...
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08/30/2018
Accelerating Parallel Tempering: Quantile Tempering Algorithm (QuanTA)
Using MCMC to sample from a target distribution, π(x) on a d-dimensional...
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08/21/2018
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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08/14/2018
Weight-Preserving Simulated Tempering
Simulated tempering is popular method of allowing MCMC algorithms to mov...
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03/26/2018
Scalable inference for crossed random effects models
We analyze the complexity of Gibbs samplers for inference in crossed ran...
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12/17/2017
Continious-time Importance Sampling: Monte Carlo Methods which Avoid Time-discretisation Error
In this paper we develop a continuous-time sequential importance samplin...
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09/22/2017
