167 citations · 623 across the 43 of their papers we have counts for
7 papers · 1 filter
Stable Matrix Parametrizations and Structured Adjoints for Ornstein-Uhlenbeck Processes
Filippo Monti, Andrew Holbrook, Nathan E. Glatt-Holtz +1
Ornstein-Uhlenbeck processes with flexible multivariate drift matrices are powerful models for capturing coupled, asymmetric, and damped-oscillatory mean reversion. However, likeli…
Mad Props: Parallelism in Markov Chain Monte Carlo Through the Lens of the Infinite Proposal Limit
Nathan E. Glatt-Holtz, Andrew J. Holbrook, Justin A. Krometis +1
Multiproposal MCMC (MP-MCMC) algorithms use clouds of proposals to efficiently traverse state spaces and overcome complex target geometries. While MCMC methods are embarrassingly p…
Multiproposal Elliptical Slice Sampling
Guillermina Senn, Nathan Glatt-Holtz, Giulia Carigi +2
We introduce Multiproposal Elliptical Slice Sampling, a self-tuning multiproposal Markov chain Monte Carlo method for Bayesian inference with Gaussian priors. Our method generalize…
Bayesian Semi-Blind Deconvolution at Scale
Guillermina Senn, Håkon Tjelmeland, Nathan Glatt-Holtz +2
Blind image deconvolution refers to the problem of simultaneously estimating the blur kernel and the true image from a set of observations when both the blur kernel and the true im…
Sacred and Profane: from the Involutive Theory of MCMC to Helpful Hamiltonian Hacks
Nathan E. Glatt-Holtz, Andrew J. Holbrook, Justin A. Krometis +2
In the first edition of this Handbook, two remarkable chapters consider seemingly distinct yet deeply connected subjects ...
On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2
Gustavo Didier, Nathan E. Glatt-Holtz, Andrew J. Holbrook +2
The continuous-time Markov chain (CTMC) is the mathematical workhorse of evolutionary biology. Learning CTMC model parameters using modern, gradient-based methods requires the deri…
Parallel MCMC Algorithms: Theoretical Foundations, Algorithm Design, Case Studies
Nathan E. Glatt-Holtz, Andrew J. Holbrook, Justin A. Krometis +1
Parallel Markov Chain Monte Carlo (pMCMC) algorithms generate clouds of proposals at each step to efficiently resolve a target probability distribution. We build a rigorous foundat…