DeeProb-kit: a Python Library for Deep Probabilistic Modelling

• URL: http://arxiv.org/abs/2212.04403v1
• Date: Thu, 8 Dec 2022 17:02:16 GMT
• Title: DeeProb-kit: a Python Library for Deep Probabilistic Modelling
• Authors: Lorenzo Loconte and Gennaro Gala
• Abstract summary: DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs) It includes efficiently implemented learning techniques, inference routines, statistical algorithms, and provides high-quality fully-documented APIs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs) that are tractable and exact representations for the modelled probability distributions. The availability of a representative selection of DPMs in a single library makes it possible to combine them in a straightforward manner, a common practice in deep learning research nowadays. In addition, it includes efficiently implemented learning techniques, inference routines, statistical algorithms, and provides high-quality fully-documented APIs. The development of DeeProb-kit will help the community to accelerate research on DPMs as well as to standardise their evaluation and better understand how they are related based on their expressivity.

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