Joint Distributions for TensorFlow Probability

A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms. We describe JointDistributions, a family of declarative representations of directed graphical models in TensorFlow Probability.

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Piponi, Dan, Moore, Dave, Dillon, Joshua V
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Computer Science - Programming Languages Statistics - Computation Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms. We describe JointDistributions, a family of declarative representations of directed graphical models in TensorFlow Probability.
DOI:	10.48550/arxiv.2001.11819