13 citations · 41 across the 21 of their papers we have counts for
10 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…
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…
Generating MCMC proposals by randomly rotating the regular simplex
Andrew J. Holbrook
We present the simplicial sampler, a class of parallel MCMC methods that generate and choose from multiple proposals at each iteration. The algorithm's multiproposal randomly rotat…
Gradients do grow on trees: a linear-time -dimensional gradient for statistical phylogenetics
Xiang Ji, Zhenyu Zhang, Andrew Holbrook +5
Calculation of the log-likelihood stands as the computational bottleneck for many statistical phylogenetic algorithms. Even worse is its gradient evaluation, often used to target r…
Massive parallelization boosts big Bayesian multidimensional scaling
Andrew Holbrook, Philippe Lemey, Guy Baele +4
Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian models for the analysis of complex phenomena in scientific inference and statis…