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

Preprints:

• Zwang T, Zhang J, Gelb-Bicknell R, Wolf N, Chanchykov V, Zhu H, Agastra E, Devine A, Holbrook A, Bennett R, Hyman B. Tau drives cell specific functional isolation of the hippocampal formation, bioRxiv preprint, (2025+): working paper.
• Ko S, Suchard M, Holbrook A. Scaling Hawkes processes to one million COVID-19 cases, arXiv preprint, (2024+): working paper.
• 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.

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, 22 (2025): 1653–1656. PDF
2. Lin C, Chen K, Lemey P, Suchard M, Holbrook A, Hsieh M. Quantum speedups for multiproposal MCMC, Bayesian Analysis, (2025): in press.
3. Sheth A, Smith A, Holbrook A. Sparse Bayesian multidimensional scaling(s), Computational Statistics, (2025): in press.
4. Williams R, Nalisnick E, Holbrook A. Scalable generative modeling of weighted graphs, Transactions of Machine Learning Research, (2025): in press.
5. Glatt-Holtz N, Harris R, Holbrook A, Krometis J, Kurniawan Y, Ringer H, Whitehead J. Embracing uncertainty in "small data" problems: estimating earthquakes from historical anecdotes, Journal of Geophysical Research: Machine Learning and Computation, 2.3 (2025): e2025JH000667. PDF
6. 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
7. 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
8. 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
9. 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
10. 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
11. Holbrook A. A quantum parallel Markov chain Monte Carlo, Journal of Computational and Graphical Statistics, 32.4 (2023): 1402-1415. PDF
12. 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
13. Holbrook A. Generating MCMC proposals by randomly rotating the regular simplex, Journal of Multivariate Analysis, 194 (2023): 105106. PDF
14. 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
15. Holbrook A, Ji X, Suchard M. From viral evolution to spatial contagion: a biologically modulated Hawkes model, Bioinformatics, 38.7 (2022): 1846-1856. PDF
16. 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
17. Tustison N, Cook P, Holbrook A, et al. ANTsX: A dynamic ecosystem for quantitative biological and medical imaging, Scientific Reports, 11.9068 (2021). PDF
18. 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
19. 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
20. 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
21. 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
22. 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
23. Holbrook A, Lumley T, Gillen D. Estimating prediction error for complex samples, Canadian Journal of Statistics, 48.2 (2020): 204-221. PDF
24. 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
25. 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
26. Li L, Holbrook A, Shahbaba B, Baldi P. Neural network gradient Hamiltonian Monte Carlo, Computational Statistics, 34.1 (2019): 281-299. PDF
27. Holbrook A. Differentiating the pseudo determinant, Linear Algebra and its Applications, 548 (2018): 293-304. PDF
28. 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
29. 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
30. 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