94 citations · 459 across the 72 of their papers we have counts for
17 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…
Scaling Hawkes processes to one million COVID-19 cases
Seyoon Ko, Marc A. Suchard, Andrew J. Holbrook
Hawkes stochastic point process models have emerged as valuable statistical tools for analyzing viral contagion. The spatiotemporal Hawkes process characterizes the speeds at which…
Efficient GPU-accelerated fitting of observational health-scaled stratified and time-varying Cox models
Jianxiao Yang, Martijn J. Schuemie, Marc A. Suchard
The Cox proportional hazards model stands as a widely-used semi-parametric approach for survival analysis in medical research and many other fields. Numerous extensions of the Cox…
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…
Many-core algorithms for high-dimensional gradients on phylogenetic trees
Karthik Gangavarapu, Xiang Ji, Guy Baele +4
The rapid growth in genomic pathogen data spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters o…
hdtg: An R package for high-dimensional truncated normal simulation
Zhenyu Zhang, Andrew Chin, Akihiko Nishimura +1
Simulating from the multivariate truncated normal distribution (MTN) is required in various statistical applications yet remains challenging in high dimensions. Currently available…