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

Preprints:

Book chapters:

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. 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, (2024): To appear in Annals of Applied Statistics.
  3. 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).
  4. 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
  5. 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
  6. Holbrook A. A quantum parallel Markov chain Monte Carlo, Journal of Computational and Graphical Statistics, 32.4 (2023): 1402-1415. PDF
  7. 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
  8. Holbrook A. Generating MCMC proposals by randomly rotating the regular simplex, Journal of Multivariate Analysis, 194 (2023): 105106. PDF
  9. 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
  10. Holbrook A, Ji X, Suchard M. From viral evolution to spatial contagion: a biologically modulated Hawkes model, Bioinformatics, 38.7 (2022): 1846-1856. PDF
  11. 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
  12. Tustison N, Cook P, Holbrook A, et al. ANTsX: A dynamic ecosystem for quantitative biological and medical imaging, Scientific Reports, 11.9068 (2021). PDF
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. Holbrook A, Lumley T, Gillen D. Estimating prediction error for complex samples, Canadian Journal of Statistics, 48.2 (2020): 204-221. PDF
  19. 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
  20. 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
  21. Li L, Holbrook A, Shahbaba B, Baldi P. Neural network gradient Hamiltonian Monte Carlo, Computational Statistics, 34.1 (2019): 281-299. PDF
  22. Holbrook A. Differentiating the pseudo determinant, Linear Algebra and its Applications, 548 (2018): 293-304. PDF
  23. 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
  24. 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
  25. 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