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
收藏
页码：25 / 27
页数：3
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