About

If you use Birch in your research, please cite the following paper:

L.M. Murray and T.B. Schön (2018). Automated learning with a probabilistic programming language: Birch. Annual Reviews in Control 46:29–43. [arxiv]

Papers

F. Ronquist, J. Kudlicka, V. Senderov, J. Borgström, N. Lartillot, D. Lundén, L.M. Murray, T.B. Schön, D. Broman. Probabilistic programming: a powerful new approach to statistical phylogenetics. [bioRXiv]
L.M. Murray (2020). Lazy object copy as a platform for population-based probabilistic programming. [arxiv]
A. Wigren, R.S. Risuleo, L.M. Murray and F. Lindsten (2019). Parameter elimination in particle Gibbs sampling. Advances in Neural Information Processing Systems. [arxiv]
J. Kudlicka, L.M. Murray, F. Ronquist and T.B. Schön (2019). Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling. Uncertainty in Artificial Intelligence. [online] [arxiv]
L.M. Murray and T.B. Schön (2018). Automated learning with a probabilistic programming language: Birch. Annual Reviews in Control 46:29–43. [arxiv]
L.M. Murray, D. Lundén, J. Kudlicka, D. Broman and T.B. Schön (2018). Delayed Sampling and Automatic Rao–Blackwellization of Probabilistic Programs. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS). [arxiv]

NeurIPS 2019 in Vancouver. Anna Wigren on Parameter elimination in particle Gibbs sampling.
PROBPROG 2018 in Boston. Lawrence Murray on Automated learning with a probabilistic programming language: Birch. [slides]
AIStats 2018 in Lanzarote. Lawrence Murray on Delayed Sampling and Automatic Rao–Blackwellization of Probabilistic Programs. [slides] [poster]
BayesComp 2018 in Barcelona. Lawrence Murray on The Birch Probabilistic Programming Language. [slides]