13 citations · 41 across the 21 of their papers we have counts for
24 papers
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…
A Spatially Varying Hierarchical Random Effects Model for Longitudinal Macular Structural Data in Glaucoma Patients
Erica Su, Robert E. Weiss, Kouros Nouri-Mahdavi +1
We model longitudinal macular thickness measurements to monitor the course of glaucoma and prevent vision loss due to disease progression. The macular thickness varies over a 6$\ti…
Random-effects substitution models for phylogenetics via scalable gradient approximations
Andrew F. Magee, Andrew J. Holbrook, Jonathan E. Pekar +7
Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present…
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…
Computational Statistics and Data Science in the Twenty-first Century
Andrew J. Holbrook, Akihiko Nishimura, Xiang Ji +1
Data science has arrived, and computational statistics is its engine. As the scale and complexity of scientific and industrial data grow, the discipline of computational statistics…