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ANOVA for Metric Spaces, with Applications to Spatial Data 度量空间方差分析及其在空间数据中的应用
IF 5.7 1区 数学 Q1 Mathematics
Statistical Science
Pub Date : 2024-05-01 DOI: 10.1214/23-sts898
Raoul Müller, Dominic Schuhmacher, Jorge Mateu
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引用次数: 1
On the Certainty of an Inductive Inference: The Binomial Case 论归纳推理的确定性：二项式案例
IF 5.7 1区 数学 Q1 Mathematics
Statistical Science
Pub Date : 2024-05-01 DOI: 10.1214/23-sts913
Frank Tuyl, Richard Gerlach, K. Mengersen
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引用次数: 0
J. B. S. Haldane’s Rule of Succession J.J. B. S. Haldane 的继承规则
IF 5.7 1区 数学 Q1 Mathematics
Statistical Science
Pub Date : 2024-05-01 DOI: 10.1214/23-sts912
Eric-Jan Wagenmakers, Sandy L. Zabell, Q. F. Gronau
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引用次数: 0
Variable Selection Using Bayesian Additive Regression Trees. 使用贝叶斯加性回归树进行变量选择。
IF 3.9 1区 数学 Q1 STATISTICS & PROBABILITY
Statistical Science
Pub Date : 2024-05-01 Epub Date: 2024-05-05 DOI: 10.1214/23-sts900
Chuji Luo, Michael J Daniels

Variable selection is an important statistical problem. This problem becomes more challenging when the candidate predictors are of mixed type (e.g. continuous and binary) and impact the response variable in nonlinear and/or non-additive ways. In this paper, we review existing variable selection approaches for the Bayesian additive regression trees (BART) model, a nonparametric regression model, which is flexible enough to capture the interactions between predictors and nonlinear relationships with the response. An emphasis of this review is on the ability to identify relevant predictors. We also propose two variable importance measures which can be used in a permutation-based variable selection approach, and a backward variable selection procedure for BART. We introduce these variations as a way of illustrating limitations and opportunities for improving current approaches and assess these via simulations.

变量选择是一个重要的统计问题。当候选预测因子为混合类型（如连续和二元），并以非线性和/或非加性方式影响响应变量时，这一问题就变得更具挑战性。在本文中，我们回顾了贝叶斯加性回归树（BART）模型的现有变量选择方法，该模型是一种非参数回归模型，具有足够的灵活性来捕捉预测因子之间的交互作用以及与响应的非线性关系。本综述的重点在于识别相关预测因子的能力。我们还提出了两种变量重要性测量方法，可用于基于置换的变量选择方法和 BART 的后向变量选择程序。我们介绍这些变式是为了说明当前方法的局限性和改进机会，并通过模拟对这些变式进行评估。
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引用次数: 0
A Conversation with Guido W. Imbens 与 Guido W. Imbens 对话
IF 5.7 1区 数学 Q1 Mathematics
Statistical Science
Pub Date : 2024-05-01 DOI: 10.1214/23-sts906
Fabrizia Mealli, Julie Holland Mortimer
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引用次数: 0
Emerging Directions in Bayesian Computation 贝叶斯计算的新方向
IF 5.7 1区 数学 Q1 Mathematics
Statistical Science
Pub Date : 2024-02-01 DOI: 10.1214/23-sts919
Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson
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引用次数: 0
In Conversation with Sir David Spiegelhalter and Professor Sylvia Richardson 对话戴维-施皮格尔哈尔特爵士和西尔维亚-理查森教授
IF 5.7 1区 数学 Q1 Mathematics
Statistical Science
Pub Date : 2024-02-01 DOI: 10.1214/23-sts897
Bhramar Mukherjee
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引用次数: 0
Past, Present and Future of Software for Bayesian Inference 贝叶斯推理软件的过去、现在和未来
IF 5.7 1区 数学 Q1 Mathematics
Statistical Science
Pub Date : 2024-02-01 DOI: 10.1214/23-sts907
Erik Štrumbelj, A. Bouchard-Côté, J. Corander, Andrew Gelman, H. Rue, Lawrence Murray, Henri Pesonen, M. Plummer, A. Vehtari
. Software tools for Bayesian inference have undergone rapid evolution in the past three decades, following popularisation of the first generation MCMC-sampler implementations. More recently, exponential growth in the number of users has been stimulated both by the active development of new packages by the machine learning community and popularity of specialist software for particular applications. This review aims to summarize the most popular software and provide a useful map for a reader to navigate the world of Bayesian computation. We anticipate a vigorous continued development of algorithms and corresponding software in multiple research fields, such as probabilistic programming, likelihood-free inference, and Bayesian neural networks, which will further broaden the possibilities for employing the Bayesian paradigm in exciting applications.
.在第一代 MCMC 采样器实现普及之后，用于贝叶斯推理的软件工具在过去三十年中经历了快速发展。最近，由于机器学习社区积极开发新软件包，以及针对特定应用的专业软件的流行，用户数量呈指数级增长。本综述旨在总结最流行的软件，为读者浏览贝叶斯计算世界提供有用的地图。我们预计，概率编程、无似然推理和贝叶斯神经网络等多个研究领域的算法和相应软件将继续蓬勃发展，这将进一步拓宽贝叶斯范式的应用范围。
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引用次数: 2
Editorial: Bayesian Computations in the 21st Century 社论：21 世纪的贝叶斯计算
IF 5.7 1区 数学 Q1 Mathematics
Statistical Science
Pub Date : 2024-02-01 DOI: 10.1214/23-sts920
Christian P. Robert, Dennis Prangle
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引用次数: 0
Diffusion Schrödinger Bridges for Bayesian Computation 用于贝叶斯计算的扩散薛定谔桥
IF 5.7 1区 数学 Q1 Mathematics
Statistical Science
Pub Date : 2024-02-01 DOI: 10.1214/23-sts908
Jeremy Heng, Valentin De Bortoli, Arnaud Doucet
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引用次数: 0
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