| The ANTsX ecosystem for quantitative biological and medical imaging NJ Tustison, PA Cook, AJ Holbrook, HJ Johnson, J Muschelli, GA Devenyi, ... Scientific reports 11 (1), 9068, 2021 | 105* | 2021 |
| Longitudinal mapping of cortical thickness measurements: An Alzheimer’s Disease Neuroimaging Initiative-based evaluation study NJ Tustison, AJ Holbrook, BB Avants, JM Roberts, PA Cook, ZM Reagh, ... Journal of Alzheimer's Disease 71 (1), 165-183, 2019 | 53 | 2019 |
| Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation A Holbrook, S Lan, A Vandenberg-Rodes, B Shahbaba Journal of statistical computation and simulation 88 (5), 982-1002, 2018 | 35 | 2018 |
| Gradients do grow on trees: a linear-time 𝒪 (N)-dimensional gradient for statistical phylogenetics X Ji, Z Zhang, A Holbrook, A Nishimura, G Baele, A Rambaut, P Lemey, ... Molecular Biology and Evolution, 2020 | 32 | 2020 |
| Massive parallelization boosts big Bayesian multidimensional scaling AJ Holbrook, P Lemey, G Baele, S Dellicour, D Brockmann, A Rambaut, ... Journal of Computational and Graphical Statistics 30 (1), 11-24, 2021 | 29 | 2021 |
| Differentiating the pseudo determinant A Holbrook Linear Algebra and its Applications 548, 293-304, 2018 | 29 | 2018 |
| Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer's disease AJ Holbrook, NJ Tustison, F Marquez, J Roberts, MA Yassa, DL Gillen, ... Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 12 (1 …, 2020 | 28 | 2020 |
| Bayesian inference on matrix manifolds for linear dimensionality reduction A Holbrook, A Vandenberg-Rodes, B Shahbaba arXiv preprint arXiv:1606.04478, 2016 | 24 | 2016 |
| Neural network gradient hamiltonian monte carlo L Li, A Holbrook, B Shahbaba, P Baldi Computational statistics 34, 281-299, 2019 | 23 | 2019 |
| A Bayesian supervised dual‐dimensionality reduction model for simultaneous decoding of LFP and spike train signals A Holbrook, A Vandenberg‐Rodes, N Fortin, B Shahbaba Stat 6 (1), 53-67, 2017 | 17 | 2017 |
| Nonparametric fisher geometry with application to density estimation A Holbrook, S Lan, J Streets, B Shahbaba Conference on Uncertainty in Artificial Intelligence, 101-110, 2020 | 12* | 2020 |
| Principled, practical, flexible, fast: A new approach to phylogenetic factor analysis GW Hassler, B Gallone, L Aristide, WL Allen, MR Tolkoff, AJ Holbrook, ... Methods in Ecology and Evolution 13 (10), 2181-2197, 2022 | 11 | 2022 |
| Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data AJ Holbrook, CE Loeffler, SR Flaxman, MA Suchard Statistics and computing 31, 1-15, 2021 | 11 | 2021 |
| Flexible bayesian dynamic modeling of correlation and covariance matrices S Lan, A Holbrook, GA Elias, NJ Fortin, H Ombao, B Shahbaba Bayesian analysis 15 (4), 1199, 2020 | 10 | 2020 |
| Attitudes toward potential participant registries JD Grill, A Holbrook, A Pierce, D Hoang, DL Gillen Journal of Alzheimer's Disease 56 (3), 939-946, 2017 | 10 | 2017 |
| From viral evolution to spatial contagion: a biologically modulated Hawkes model AJ Holbrook, X Ji, MA Suchard Bioinformatics 38 (7), 1846-1856, 2022 | 9 | 2022 |
| Bayesian mitigation of spatial coarsening for a Hawkes model applied to gunfire, wildfire and viral contagion AJ Holbrook, X Ji, MA Suchard The annals of applied statistics 16 (1), 573, 2022 | 8* | 2022 |
| A quantum parallel Markov chain Monte Carlo AJ Holbrook Journal of Computational and Graphical Statistics 32 (4), 1402-1415, 2023 | 7 | 2023 |
| Parallel MCMC algorithms: Theoretical foundations, algorithm design, case studies NE Glatt-Holtz, AJ Holbrook, JA Krometis, CF Mondaini arXiv preprint arXiv:2209.04750, 2022 | 6 | 2022 |
| Generating MCMC proposals by randomly rotating the regular simplex AJ Holbrook Journal of multivariate analysis 194, 105106, 2023 | 5 | 2023 |
Marc A. SuchardProfessor, University of California, Los AngelesVerified email at ucla.edu
