学习 DAG 的 Gibbs 采样器

IF 4.3 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Journal of Machine Learning Research Pub Date : 2016-04-01
Robert J B Goudie, Sach Mukherjee
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引用次数: 0

摘要

我们提出了一种用于有向无环图(DAG)模型结构学习的吉布斯采样器。用于 DAG 学习的标准马尔可夫链蒙特卡罗算法是随机漫步 Metropolis-Hastings 采样器。这些采样器保证渐进收敛,但在探索结构学习中出现的大型图空间时,往往混合缓慢。在每一步中,我们提出的采样器都会从适当的条件分布中为多个节点抽取整套父节点。这提供了一种在图空间中进行大规模移动的有效方法,既能加快混合速度,又能保持渐进保证的收敛性。条件分布与变量选择有关,候选父节点扮演协变量或输入的角色。我们利用几个模拟和真实数据实例对采样器的性能进行了实证检验。所提出的方法在不同的环境下都能提供稳健的结果,其性能优于现有的几种贝叶斯和频数方法。此外,我们的实证结果还揭示了贝叶斯方法和基于约束的结构学习方法的相对优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Gibbs Sampler for Learning DAGs.

A Gibbs Sampler for Learning DAGs.

A Gibbs Sampler for Learning DAGs.

We propose a Gibbs sampler for structure learning in directed acyclic graph (DAG) models. The standard Markov chain Monte Carlo algorithms used for learning DAGs are random-walk Metropolis-Hastings samplers. These samplers are guaranteed to converge asymptotically but often mix slowly when exploring the large graph spaces that arise in structure learning. In each step, the sampler we propose draws entire sets of parents for multiple nodes from the appropriate conditional distribution. This provides an efficient way to make large moves in graph space, permitting faster mixing whilst retaining asymptotic guarantees of convergence. The conditional distribution is related to variable selection with candidate parents playing the role of covariates or inputs. We empirically examine the performance of the sampler using several simulated and real data examples. The proposed method gives robust results in diverse settings, outperforming several existing Bayesian and frequentist methods. In addition, our empirical results shed some light on the relative merits of Bayesian and constraint-based methods for structure learning.

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来源期刊
Journal of Machine Learning Research
Journal of Machine Learning Research 工程技术-计算机:人工智能
CiteScore
18.80
自引率
0.00%
发文量
2
审稿时长
3 months
期刊介绍: The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
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