Shiwei Lan
Shiwei Lan
Verified email at - Homepage
Cited by
Cited by
Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high‐resolution simulations
T Schneider, S Lan, A Stuart, J Teixeira
Geophysical Research Letters 44 (24), 12,396-12,417, 2017
Geometric MCMC for infinite-dimensional inverse problems
A Beskos, M Girolami, S Lan, PE Farrell, AM Stuart
Journal of Computational Physics 335, 327-351, 2017
Calibrate, emulate, sample
E Cleary, A Garbuno-Inigo, S Lan, T Schneider, AM Stuart
Journal of Computational Physics 424, 109716, 2021
Split hamiltonian monte carlo
B Shahbaba, S Lan, WO Johnson, RM Neal
Statistics and Computing 24, 339-349, 2014
Wormhole hamiltonian monte carlo
S Lan, J Streets, B Shahbaba
Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014
Spherical Hamiltonian Monte Carlo for constrained target distributions
S Lan, B Zhou, B Shahbaba
International Conference on Machine Learning, 629-637, 2014
phylodyn: an R package for phylodynamic simulation and inference
MD Karcher, JA Palacios, S Lan, VN Minin
Molecular ecology resources 17 (1), 96-100, 2017
Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian inverse problems
S Lan, T Bui-Thanh, M Christie, M Girolami
Journal of Computational Physics 308, 81-101, 2016
Markov chain monte carlo from lagrangian dynamics
S Lan, V Stathopoulos, B Shahbaba, M Girolami
Journal of Computational and Graphical Statistics 24 (2), 357-378, 2015
An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics
S Lan, JA Palacios, M Karcher, VN Minin, B Shahbaba
Bioinformatics 31 (20), 3282-3289, 2015
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
Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry
T House, A Ford, S Lan, S Bilson, E Buckingham-Jeffery, M Girolami
Journal of the Royal Society Interface 13 (121), 20160279, 2016
Sampling constrained probability distributions using spherical augmentation
S Lan, B Shahbaba
Algorithmic Advances in Riemannian Geometry and Applications: For Machine …, 2016
Scaling up bayesian uncertainty quantification for inverse problems using deep neural networks
S Lan, S Li, B Shahbaba
SIAM/ASA Journal on Uncertainty Quantification 10 (4), 1684-1713, 2022
Adaptive dimension reduction to accelerate infinite-dimensional geometric Markov Chain Monte Carlo
S Lan
Journal of Computational Physics 392, 71-95, 2019
Lagrangian Dynamical Monte Carlo
S Lan, V Stathopoulos, B Shahbaba, M Girolami
arXiv preprint arXiv:1211.3759, 2012
A semiparametric Bayesian model for detecting synchrony among multiple neurons
B Shahbaba, B Zhou, S Lan, H Ombao, D Moorman, S Behseta
Neural computation 26 (9), 2025-2051, 2014
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
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
Deep markov chain monte carlo
B Shahbaba, LM Lomeli, T Chen, S Lan
arXiv preprint arXiv:1910.05692, 2019
The system can't perform the operation now. Try again later.
Articles 1–20