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

Preprints:

• Lin C, Chen K, Lemey P, Suchard M, Holbrook A, Hsieh M. Quantum speedups for multiproposal MCMC, arXiv preprint, (2023): submitted to Bayesian Analysis.
• 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, arXiv preprint, (2023): submitted to Systematic Biology.
• Li Y, Ghafari M, Holbrook A, Boonen I, Amor N, Catalano S, Webster J, et al. The evolutionary history of hepaciviruses, bioRxiv preprint, (2023): submitted to PNAS.
• Su E, Weiss R, Nouri-Mahdavi K, Holbrook A. A spatially varying hierarchical random effects model for longitudinal macular structural data in glaucoma patients, arXiv preprint, (2023): submitted to the Annals of Applied Statistics.
• 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): working paper.
• Glatt-Holtz N, Holbrook A, Krometis J, Mondaini C. Parallel MCMC algorithms: theoretical foundations, algorithm design, case studies, arXiv preprint, (2022): submitted to Transactions of Mathematics and its Applications.

Book chapters:

• 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. 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
2. 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, medRxiv preprint, (2024): To appear in Scientific Reports.
3. Holbrook A. A quantum parallel Markov chain Monte Carlo, Journal of Computational and Graphical Statistics, 32.4 (2023): 1402-1415. PDF
4. 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
5. Holbrook A. Generating MCMC proposals by randomly rotating the regular simplex, Journal of Multivariate Analysis, 194 (2023): 105106. PDF
6. 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
7. Holbrook A, Ji X, Suchard M. From viral evolution to spatial contagion: a biologically modulated Hawkes model, Bioinformatics, 38.7 (2022): 1846-1856. PDF
8. 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
9. Tustison N, Cook P, Holbrook A, et al. ANTsX: A dynamic ecosystem for quantitative biological and medical imaging, Scientific Reports, 11.9068 (2021). PDF
10. 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
11. 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
12. 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
13. 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
14. 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
15. Holbrook A, Lumley T, Gillen D. Estimating prediction error for complex samples, Canadian Journal of Statistics, 48.2 (2020): 204-221. PDF
16. 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
17. 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
18. Li L, Holbrook A, Shahbaba B, Baldi P. Neural network gradient Hamiltonian Monte Carlo, Computational Statistics, 34.1 (2019): 281-299. PDF
19. Holbrook A. Differentiating the pseudo determinant, Linear Algebra and its Applications, 548 (2018): 293-304. PDF
20. 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
21. 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
22. 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