probcomp / programmable-vi-pldi-2024
Probabilistic programming with programmable variational inference.
☆20Updated 9 months ago
Alternatives and similar repositories for programmable-vi-pldi-2024:
Users that are interested in programmable-vi-pldi-2024 are comparing it to the libraries listed below
- Inference Combinators in JAX☆46Updated 5 months ago
- Probabilistic programming system for fast and exact symbolic inference☆79Updated 8 months ago
- Exponential families for JAX☆63Updated this week
- Probabilistic Circuits from the Juice library☆106Updated 9 months ago
- ☆52Updated last year
- Loopy belief propagation for factor graphs on discrete variables, in JAX!☆65Updated 5 months ago
- A curated collection of papers on probabilistic circuits, computational graphs encoding tractable probability distributions.☆49Updated last year
- Scalable training and inference for Probabilistic Circuits☆56Updated last month
- Generic API for dispatch to Pyro backends.☆16Updated 3 years ago
- A generic interface for linear algebra backends☆71Updated 3 weeks ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 3 years ago
- Causal, Higher-Order, Probabilistic Programming☆166Updated 2 years ago
- probabilistic programming focused on fun☆40Updated 5 months ago
- ☆80Updated 3 years ago
- ☆15Updated 6 years ago
- First-order knowledge compilation for lifted probabilistic inference☆11Updated 7 years ago
- Implementation of Nonparametric Hamiltonian Monte Carlo☆12Updated 2 years ago
- GAP package for Hierarchical Composition and Decomposition of Permutation Groups and Transformation Semigroups☆19Updated 2 weeks ago
- Code for efficiently sampling functions from GP(flow) posteriors☆70Updated 4 years ago
- Exact inference for discrete probabilistic programs. (Research code, more documentation and ergonomics to come)☆83Updated last week
- Bayesian inference with Python and Jax.☆32Updated 2 years ago
- "Discontinuous Hamiltonian Monte Carlo for sampling discrete parameters" by Akihiko Nishimura, David Dunson, Jianfeng Lu☆27Updated 6 years ago
- Learning Algebraic Varieties from Samples☆24Updated last year
- Modelling epidemiological dynamics and performing inference in these models☆27Updated 3 years ago
- Practical tools for quantifying how well a sample approximates a target distribution☆27Updated 4 years ago
- A Julia implementation of sparse Gaussian processes via path-wise doubly stochastic variational inference.☆33Updated 4 years ago
- ☆19Updated last week
- AeMCMC is a Python library that automates the construction of samplers for Aesara graphs representing statistical models.☆39Updated last year
- equation discovery based on generative models☆15Updated 2 months ago
- ☆20Updated 4 months ago