Doctoral thesis:  Holbrook A. Geometric Bayes, Diss. UC Irvine, 2018. Advisor: Prof. Babak Shahbaba, Ph.D.

Preprints:

• Williams R, Nalisnick E, Holbrook A. Scalable generative modeling of weighted graphs, (2025+): submitted to UAI2025.
• Ko S, Suchard M, Holbrook A. Scaling Hawkes processes to one million COVID-19 cases, arXiv preprint, (2024+): submitted to Statistics and Computing.
• Sheth A, Smith A, Holbrook A. Sparse Bayesian multidimensional scaling(s), arXiv preprint, (2024+): submitted to Statistics and Computing.
• Lin C, Chen K, Lemey P, Suchard M, Holbrook A, Hsieh M. Quantum speedups for multiproposal MCMC, arXiv preprint, (2023+): submitted to Bayesian Analysis.
• Li Y, Ghafari M, Holbrook A, Boonen I, Amor N, Catalano S, Webster J, et al. The evolutionary history of hepaciviruses, bioRxiv preprint, (2023+): working paper.
• Krometis J, Ringer H, Whitehead J, Glatt-Holtz N, Harris R, Holbrook A. Embracing uncertainty in "small data" problems: estimating earthquakes from historical anecdotes, arXiv preprint, (2022+): submitted to Journal of Geophysical Research - Machine Learning and Computation.

Book chapters:

1. Glatt-Holtz N, Holbrook A, Krometis J, Mondaini C, Sheth A. "Sacred and profane: from the involutive theory of MCMC to helpful Hamiltonian hacks." In Handbook of Markov Chain Monte Carlo, Second Edition (2024+). arXiv preprint
2. Holbrook A, Nishimura A, Ji X, Suchard M. "Computational statistics and data science in the twenty-first century." In Piegorsch, W.W., Levine, R.A., Zhang, H.H., and Lee, T.C.M. (eds.). Computational Statistics in Data Science, (2022): John Wiley & Sons. DOI, PDF

Refereed publications:

1. Baele G, Ji X, Hassler G, McCrone J, Shao Y, Zhang Z, Holbrook A, Lemey P, Drummond A, Rambaut A, Suchard M. BEAST X for Bayesian phylogenetic, phylogeographic and phylodynamic inference, Nature Methods (2025): in press.
2. Didier G, Glatt-Holtz N, Holbrook A, Magee A, Suchard M. On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2, Proceedings of the National Academy of Sciences, 121.3 (2024): e2318989121. PDF
3. Su E, Weiss R, Nouri-Mahdavi K, Holbrook A. A spatially varying hierarchical random effects model for longitudinal macular structural data in glaucoma patients, Annals of Applied Statistics, 18.4 (2024): 3444-3466. PDF
4. Glatt-Holtz N, Holbrook A, Krometis J, Mondaini C. Parallel MCMC algorithms: theoretical foundations, algorithm design, case studies, Transactions of Mathematics and its Applications, 8.2 (2024). PDF
5. Magee A, Holbrook A, Pekar J, Caviedes-Solis I, Matsen F IV, Baele G, Wertheim J, Ji X, Lemey P, Suchard M. Random-effects substitution models for phylogenetics via scalable gradient approximations, Systematic Biology, 73.3 (2024): 562–578. PDF
6. Tustison N, Yassa M, Rizvi B, Cook P, Holbrook A, Sathishkumar M, Tustison M, Gee J, Stone J, Avants B. ANTsX neuroimaging-derived structural phenotypes of UK Biobank, Scientific Reports, 14.8848 (2024). PDF
7. Holbrook A. A quantum parallel Markov chain Monte Carlo, Journal of Computational and Graphical Statistics, 32.4 (2023): 1402-1415. PDF
8. Zhang Z, Nishimura A, Trovão S, Cherry J, Holbrook A, Ji X, Lemey P, Suchard M. Accelerating Bayesian inference of dependency between mixed-type biological traits, PLOS Computational Biology, 19.8 (2023): e1011419. PDF
9. Holbrook A. Generating MCMC proposals by randomly rotating the regular simplex, Journal of Multivariate Analysis, 194 (2023): 105106. PDF
10. Hassler G, Gallone B, Aristide L, Allen W, Tolkoff M, Holbrook A, Baele G, Lemey P, Suchard M. Principled, practical, flexible, fast: a new approach to phylogenetic factor analysis, Methods in Ecology and Evolution, 13 (2022): 2181-2197. PDF
11. Holbrook A, Ji X, Suchard M. From viral evolution to spatial contagion: a biologically modulated Hawkes model, Bioinformatics, 38.7 (2022): 1846-1856. PDF
12. Holbrook A, Ji X, Suchard M. Bayesian mitigation of spatial coarsening for a Hawkes model applied to gunfire, wildfire and viral contagion, Annals of Applied Statistics, 16.1 (2022): 573-595. PDF
13. Tustison N, Cook P, Holbrook A, et al. ANTsX: A dynamic ecosystem for quantitative biological and medical imaging, Scientific Reports, 11.9068 (2021). PDF
14. Holbrook A, Loeffler C, Flaxman S, Suchard M. Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data, Statistics and Computing, 31.4 (2021). PDF
15. Holbrook A, Lemey P, Baele G, Dellicour S, Brockmann D, Rambaut A, Suchard M. Massive parallelization boosts big Bayesian multidimensional scaling, Journal of Computational and Graphical Statistics, 30.1 (2021): 11-24. PDF
16. Holbrook A, Tustison N, Marquez F, Roberts J, Yassa M, Gillen D. Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer’s disease, Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 12.1 (2020). PDF
17. Ji X, Zhang Z, Holbrook A, Nishimura A, Baele G, Rambaut A, Lemey P, Suchard M. Gradients do grow on trees: a linear-time O(N)-dimensional gradient for statistical phylogenetics, Molecular Biology and Evolution, 37.10 (2020): 3047–3060. PDF
18. Shahbaba B, Lan S, Streets J, Holbrook A. Nonparametric Fisher geometry with application to density estimation, Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in PMLR 124 (2020): 101-110. PDF
19. Holbrook A, Lumley T, Gillen D. Estimating prediction error for complex samples, Canadian Journal of Statistics, 48.2 (2020): 204-221. PDF
20. Lan S, Holbrook A, Elias G, Fortin N, Ombao H, Shahbaba B. Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices, Bayesian Analysis, 15.4 (2020): 1199-1228. PDF
21. Tustison N, Holbrook A, Avants B, Roberts J, Cook P, Reagh Z, Stone J, Gillen D, Yassa M. Longitudinal mapping of cortical thickness measurements: an Alzheimer’s Disease Neuroimaging Initiative-based evaluation study, Journal of Alzheimer's Disease, 71.1 (2019): 165-183. PDF
22. Li L, Holbrook A, Shahbaba B, Baldi P. Neural network gradient Hamiltonian Monte Carlo, Computational Statistics, 34.1 (2019): 281-299. PDF
23. Holbrook A. Differentiating the pseudo determinant, Linear Algebra and its Applications, 548 (2018): 293-304. PDF
24. Holbrook A, Lan S, Vandenberg-Rodes A, Shahbaba B. Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation, Journal of Statistical Computation and Simulation, 88.5 (2018): 982-1002. PDF
25. Holbrook A, Vandenberg-Rodes A, Fortin N, Shahbaba B. A Bayesian supervised dual‐dimensionality reduction model for simultaneous decoding of LFP and spike train signals, Stat, 6.1 (2017): 53-67. PDF
26. Grill J, Holbrook A, Pierce A, Hoang D, Gillen D. Attitudes toward potential participant registries, Journal of Alzheimer's Disease, 56.3 (2017): 939-946. PDF