Bayesian network models for incomplete and dynamic data

被引：23

作者：

Scutari, Marco ^{[1
]}

机构：

[1] Ist Dalle Molle Studi Intelligenza Artificiale ID, Galleria 2,Via Cantonale 2c, CH-6928 Manno, Switzerland

来源：

STATISTICA NEERLANDICA | 2020年 / 74卷 / 03期

关键词：

Bayesian networks; dynamic data; incomplete data; inference; structure learning; STRUCTURAL EM ALGORITHM; TIME-SERIES; DECISION-SUPPORT; GENE NETWORKS; MARKOV-MODELS; INFERENCE; DISTRIBUTIONS; OPTIMIZATION; INFLAMMATION; REGRESSION;

D O I：

10.1111/stan.12197

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper, we will review how Bayesian networks can model dynamic data and data with incomplete observations. Such data are the norm at the forefront of research and in practical applications, and Bayesian networks are uniquely positioned to model them due to their explainability and interpretability.

引用

页码：397 / 419

页数：23

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