Automated learning with a probabilistic programming language: Birch

2 October 2018

Papers citing "Automated learning with a probabilistic programming language: Birch"

10 / 10 papers shown

Title
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method A. Wigren Johan Wågberg Fredrik Lindsten A. Wills Thomas B. Schon 24 10 0 26 Oct 2022
Nested Variational Inference Heiko Zimmermann Hao Wu Babak Esmaeili Jan-Willem van de Meent BDL 32 20 0 21 Jun 2021
Conditional independence by typing Maria I. Gorinova Andrew D. Gordon Charles Sutton Matthijs Vákár 27 14 0 22 Oct 2020
An invitation to sequential Monte Carlo samplers Chenguang Dai J. Heng Pierre E. Jacob N. Whiteley 57 65 0 23 Jul 2020
Stochastically Differentiable Probabilistic Programs David Tolpin Yuanshuo Zhou Hongseok Yang BDL 11 0 0 02 Mar 2020
Lazy object copy as a platform for population-based probabilistic programming Lawrence M. Murray 21 5 0 09 Jan 2020
Parameter elimination in particle Gibbs sampling A. Wigren Riccardo Sven Risuleo Lawrence M. Murray Fredrik Lindsten 27 15 0 30 Oct 2019
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support Yuanshuo Zhou Hongseok Yang Yee Whye Teh Tom Rainforth TPM 29 19 0 29 Oct 2019
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling J. Kudlicka Lawrence M. Murray F. Ronquist Thomas B. Schon 26 10 0 10 Jul 2019
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data J. Bierkens Paul Fearnhead Gareth O. Roberts 58 231 0 11 Jul 2016