Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities

7 Apr 2018 • Bradley Gram-Hansen • Yuan Zhou • Tobias Kohn • Tom Rainforth • Hongseok Yang • Frank Wood

Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs). However, the fact that HMC uses derivative information causes complications when the target distribution is non-differentiable with respect to one or more of the latent variables... (read more)

PDF Abstract

Results from the Paper

Add Remove

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

METHOD	TYPE
🤖 No Methods Found	Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet

Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Add Remove

Methods used in the Paper

Add Remove

Edit Social Preview

Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities Edit social preview

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit Add Remove

Methods used in the Paper Edit Add Remove

Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Add Remove

Methods used in the Paper

Add Remove