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THRML (Thermodynamic Hypergraphical Model Library) is a JAX-based Python library designed for building and efficiently sampling probabilistic graphical models (PGMs). Developed by Extropic, it focuses
This guide will walk you through the installation of the THRML library and provide a detailed explanation of a quick example demonstrating its core functionalities. THRML is designed to facilitate the
THRML (Thermodynamic Hypergraphical Model Library) is built upon a set of fundamental abstractions that collectively enable the definition, representation, and efficient sampling of probabilistic grap
This page details the core classes and functions responsible for managing blocks of nodes and orchestrating the sampling process in THRML. These components are fundamental to how THRML handles probabi
This page provides a detailed API reference for the core modeling components in THRML, including factors, interactions, energy-based models (EBMs), and conditional samplers. These components are essen
Ising models are fundamental in statistical mechanics and machine learning, particularly as a foundational type of [Energy-Based Model (EBM)](/wiki/extropic-ai/thrml/api-models-factors#energy-based-mo
This guide demonstrates how to build and sample a discrete Energy-Based Model (EBM) in THRML, featuring a mix of `SpinNode` (binary) and `CategoricalNode` variables. This type of mixed model is common