Download
This module provides a Julia implementation of Probabilistic Circuits (PCs), tools to learn structure and parameters of PCs from data, and tools to do tractable exact inference with them. Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units). Given certain structural properties, PCs enable different range of tractable exact probabilistic queries such as computing marginals, conditionals, maximum a posteriori (MAP), and more advanced probabilistic queries.
Features
- In additon to parameters, the structure of PCs can also be learned from data
- There are several approaches in learning PCs, while keeping the needed structural constrains intact
- Currently, This module includes implementation for few of these approaches with plans to add more over time
- Additionally, parallelism (on both CPU and GPU) is leveraged to provide faster implementation of learning and inference
- Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models
- PCs are represented as a computational graphs that define a joint probability distribution as recursive mixtures
Project Samples
License
Apache License V2.0Follow ProbabilisticCircuits.jl
Other Useful Business Software
Enterprise-grade ITSM, for every business
Freshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of ProbabilisticCircuits.jl!