ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1903.02482
  4. Cited By
LF-PPL: A Low-Level First Order Probabilistic Programming Language for
  Non-Differentiable Models

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models

6 March 2019
Yuanshuo Zhou
Bradley Gram-Hansen
Tobias Kohn
Tom Rainforth
Hongseok Yang
Frank D. Wood
ArXivPDFHTML

Papers citing "LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models"

6 / 6 papers shown
Title
Guaranteed Bounds for Posterior Inference in Universal Probabilistic
  Programming
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming
Raven Beutner
Luke Ong
Fabian Zaiser
14
11
0
06 Apr 2022
Conditional independence by typing
Conditional independence by typing
Maria I. Gorinova
Andrew D. Gordon
Charles Sutton
Matthijs Vákár
17
14
0
22 Oct 2020
Stochastically Differentiable Probabilistic Programs
Stochastically Differentiable Probabilistic Programs
David Tolpin
Yuanshuo Zhou
Hongseok Yang
BDL
4
0
0
02 Mar 2020
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic
  Programs with Stochastic Support
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Yuanshuo Zhou
Hongseok Yang
Yee Whye Teh
Tom Rainforth
TPM
16
19
0
29 Oct 2019
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous
  Variables
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables
Guangyao Zhou
14
19
0
11 Sep 2019
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
182
3,262
0
09 Jun 2012
1