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11/13/2022
Multivariate strong invariance principles in Markov chain Monte Carlo
Strong invariance principles in Markov chain Monte Carlo are crucial to ...
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10/24/2022
Understanding Linchpin Variables in Markov Chain Monte Carlo
An introduction to the use of linchpin variables in Markov chain Monte...
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06/12/2022
Solving the Poisson equation using coupled Markov chains
This article draws connections between unbiased estimators constructed f...
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08/30/2021
A principled stopping rule for importance sampling
Importance sampling (IS) is a Monte Carlo technique that relies on weigh...
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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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03/29/2021
Variational Rejection Particle Filtering
We present a variational inference (VI) framework that unifies and lever...
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01/12/2021
Bayesian equation selection on sparse data for discovery of stochastic dynamical systems
Often the underlying system of differential equations driving a stochast...
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09/03/2020
Globally-centered autocovariances in MCMC
Autocovariances are a fundamental quantity of interest in Markov chain M...
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07/08/2020
Estimating Monte Carlo variance from multiple Markov chains
The ever-increasing power of the personal computer has led to easy paral...
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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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07/26/2019
Analyzing MCMC Output
Markov chain Monte Carlo (MCMC) is a sampling-based method for estimatin...
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04/26/2019
New visualizations for Monte Carlo simulations
In Monte Carlo simulations, samples are obtained from a target distribut...
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12/21/2018
Revisiting the Gelman-Rubin Diagnostic
Gelman and Rubin's (1992) convergence diagnostic is one of the most popu...
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09/12/2018