Probabilistic programming: a powerful new approach to statistical phylogenetics’
F. Ronquist, J. Kudlicka, V. Senderov, J. Borgström, N. Lartillot, D. Lundén, L.M. Murray, T.B. Schön, D. Broman
2020-06-23
phylogenetics probabilistic programming Birch
2020-02-01
memory management probabilistic programming Birch
Parameter elimination in particle Gibbs sampling’
A. Wigren, R.S. Risuleo, L.M. Murray and F. Lindsten
2019-11-09
particle Gibbs delayed sampling probabilistic programming Birch
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling’
J. Kudlicka, L.M. Murray, F. Ronquist and T.B. Schön
2019-07-14
probabilistic programming particle filter delayed sampling phylogenetics Birch
Automated learning with a probabilistic programming language: Birch’
L.M. Murray and T.B. Schön
2018-10-13
Birch probabilistic programming sequential Monte Carlo delayed sampling
Improving the particle filter for high-dimensional problems using artificial process noise’
A. Wigren, L.M. Murray, F. Lindsten
2018-03-01
high dimensional sequential Monte Carlo
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs’
L.M. Murray, D. Lundén, J. Kudlicka, D. Broman, T.B. Schön
2017-12-22
probabilistic programming sequential Monte Carlo delayed sampling
Better together? Statistical learning in models made of modules’
P.E. Jacob, L.M. Murray, C.C. Holmes, C.P. Robert
2017-08-29
Bayesian statistics modules
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo’
T.B. Schön, A. Svensson, L.M. Murray, and F. Lindsten
2017-03-07
tutorial particle filter sequential Monte Carlo
2016-12-10
parallel distributed gpu cloud computing particle filter Bayesian statistics
Comparative Analysis of Dengue and Zika Outbreaks Reveals Differences by Setting and Virus
S. Funk, A.J. Kucharski, A. Camacho, R.M. Eggo, L. Yakob, L.M. Murray, and W.J. Edmunds
2016-12-07
2015-03-01