Probabilistic Programming
87 papers with code • 0 benchmarks • 0 datasets
Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.
( Image credit: Michael Betancourt )
Benchmarks
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Libraries
Use these libraries to find Probabilistic Programming models and implementationsLatest papers
BlackJAX: Composable Bayesian inference in JAX
BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation.
SymbolicAI: A framework for logic-based approaches combining generative models and solvers
We conclude by introducing a quality measure and its empirical score for evaluating these computational graphs, and propose a benchmark that compares various state-of-the-art LLMs across a set of complex workflows.
Diffusion models for probabilistic programming
dirmeier/dmvi
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We propose Diffusion Model Variational Inference (DMVI), a novel method for automated approximate inference in probabilistic programming languages (PPLs).
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
Our architecture integrates two computational tools that have not previously come together: we model thinking with probabilistic programs, an expressive representation for commonsense reasoning; and we model meaning construction with large language models (LLMs), which support broad-coverage translation from natural language utterances to code expressions in a probabilistic programming language.
Scalable Neural-Probabilistic Answer Set Programming
The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI.
Push: Concurrent Probabilistic Programming for Bayesian Deep Learning
lbai-lab/push
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We introduce a library called Push that takes a probabilistic programming approach to Bayesian deep learning (BDL).
Automating Model Comparison in Factor Graphs
Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages.
Bayesian Calibration of MEMS Accelerometers
This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical systems (MEMS) accelerometers.
Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs
Even after fine-tuning and reinforcement learning, large language models (LLMs) can be difficult, if not impossible, to control reliably with prompts alone.
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors.
