InferPy: Probabilistic modeling with Tensorflow made easy

被引：7

作者：

Cabanas, Rafael ^{[1
]}

Salmeron, Antonio ^{[1
]}

Masegosa, Andres R. ^{[1
]}

机构：

[1] Univ Almeria, ES-04120 Almeria, Spain

来源：

KNOWLEDGE-BASED SYSTEMS | 2019年 / 168卷

关键词：

Probabilistic programming; Hierarchical probabilistic models; Latent variables; Tensorflow; User-friendly;

D O I：

10.1016/j.knosys.2018.12.030

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

InferPy is a high-level Python API for probabilistic modeling built on top of Edward and Tensorflow. InferPy, which is strongly inspired by Keras, focuses on being user-friendly by using an intuitive set of abstractions that make easy to deal with complex probabilistic models. It should be seen as an interface rather than a standalone machine-learning framework. In general, InferPy has the focus on enabling flexible data processing, easy-to-code probabilistic modeling, scalable inference and robust model validation. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：25 / 27

页数：3

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