{"title":"Python中的贝叶斯建模与计算","authors":"Shuai Huang","doi":"10.1080/00224065.2022.2041379","DOIUrl":null,"url":null,"abstract":"This book is useful for readers who want to hone their skills in Bayesian modeling and computation. Written by experts in the area of Bayesian software and major contributors to some existing widely used Bayesian computational tools, this book covers not only basic Bayesian probabilistic inference but also a range of models from linear models (and mixed effect models, hierarchical models, splines, etc) to time series models such as the state space model. It also covers the Bayesian additive regression trees. Almost all the concepts and techniques are implemented using PyMC3, Tensorflow Probability (TFP), ArviZ and other libraries. By doing all the modeling, computation, and data analysis, the authors not only show how these things work, but also show how and why things don’t work by emphasis on exploratory data analysis, model comparison, and diagnostics. To learn from the book, readers may need some statistical background such as basic training in statistics and probability theory. Some understanding of Bayesian modeling and inference is also needed, such as the concepts of prior, likelihood, posterior, the bayes’s law, and Monte Carlo sampling. Some experience with Python would also be very beneficial for readers to get started on this journey of Bayesian modeling. The authors suggested a few books as possible preliminaries for their book. I feel that the readers may also benefit from reading Andrew Gelman’s book, Bayesian Data Analysis, Chapman & Hall/CRC, 3rd Edition, 2013. Of course, as the authors pointed it out, this book is not for a Bayesian Reader but a Bayesian practitioner. The book is more of an interactive experience for Bayesian practitioners by learning all the computational tools to model and to negotiate with data for a good modeling practice. On the other hand, if readers have already had experience with real-world data analysis using Python or R or other similar tools, even if this book is their first experience with Bayesian modeling and computation, readers may still learn a lot from this book. There are an abundance of figures and detailed explanations of how things are done and how the results are interpreted. Picking up these details would need some trained sensibility when dealing with real-world data, but aspiring and experienced practitioners should find all the details useful and impressive. And there are also many big picture schematic drawings to help readers connect all the details with overall concepts such as end-to-end workflows. The Figure 9.1 is a remarkable example. Overall, as Kevin Murphy pointed out in the Forward, “this is a valuable addition to the literature, which should hopefully further the adoption of Bayesian methods”. I highly recommend readers who are interested in learning Bayesian models and their applications in practice to have this book on their bookshelf.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Bayesian Modeling and Computation in Python\",\"authors\":\"Shuai Huang\",\"doi\":\"10.1080/00224065.2022.2041379\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This book is useful for readers who want to hone their skills in Bayesian modeling and computation. 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引用次数: 16
摘要
这本书对想要磨练贝叶斯建模和计算技能的读者很有用。由贝叶斯软件领域的专家和一些现有广泛使用的贝叶斯计算工具的主要贡献者撰写,本书不仅涵盖了基本的贝叶斯概率推断,还涵盖了从线性模型(和混合效应模型,层次模型,样条等)到时间序列模型(如状态空间模型)的一系列模型。它还涵盖了贝叶斯加性回归树。几乎所有的概念和技术都是使用PyMC3、Tensorflow Probability (TFP)、ArviZ和其他库实现的。通过进行所有的建模、计算和数据分析,作者不仅展示了这些东西是如何工作的,而且通过强调探索性数据分析、模型比较和诊断,还展示了事情是如何以及为什么不工作的。为了从这本书中学习,读者可能需要一些统计背景,如统计和概率论的基本训练。还需要对贝叶斯建模和推理有一定的了解,例如先验、似然、后验、贝叶斯定律和蒙特卡罗抽样的概念。对于开始贝叶斯建模之旅的读者来说,一些Python的经验也是非常有益的。作者们推荐了几本书作为他们这本书可能的序言。我觉得读者也可以从Andrew Gelman的书中受益,Bayesian Data Analysis, Chapman & Hall/CRC, 3rd Edition, 2013。当然,正如作者所指出的,这本书不是为贝叶斯读者而写,而是为贝叶斯实践者而写。这本书更多的是贝叶斯实践者的互动体验,通过学习所有的计算工具来建模和与数据协商,以获得良好的建模实践。另一方面,如果读者已经有了使用Python或R或其他类似工具进行实际数据分析的经验,即使本书是他们第一次使用贝叶斯建模和计算,读者仍然可以从本书中学到很多东西。书中有大量的数据和详细的解释,说明事情是如何完成的,结果是如何解释的。在处理真实世界的数据时,获取这些细节需要一些训练有素的敏感性,但是有抱负和经验丰富的从业者应该会发现所有的细节都是有用的和令人印象深刻的。此外,还有许多大图原理图,帮助读者将所有细节与整体概念(如端到端工作流)联系起来。图9.1就是一个很好的例子。总的来说,正如Kevin Murphy在前言中指出的,“这是对文献的一个有价值的补充,它应该有望进一步采用贝叶斯方法”。我强烈建议对学习贝叶斯模型及其在实践中的应用感兴趣的读者把这本书放在书架上。
This book is useful for readers who want to hone their skills in Bayesian modeling and computation. Written by experts in the area of Bayesian software and major contributors to some existing widely used Bayesian computational tools, this book covers not only basic Bayesian probabilistic inference but also a range of models from linear models (and mixed effect models, hierarchical models, splines, etc) to time series models such as the state space model. It also covers the Bayesian additive regression trees. Almost all the concepts and techniques are implemented using PyMC3, Tensorflow Probability (TFP), ArviZ and other libraries. By doing all the modeling, computation, and data analysis, the authors not only show how these things work, but also show how and why things don’t work by emphasis on exploratory data analysis, model comparison, and diagnostics. To learn from the book, readers may need some statistical background such as basic training in statistics and probability theory. Some understanding of Bayesian modeling and inference is also needed, such as the concepts of prior, likelihood, posterior, the bayes’s law, and Monte Carlo sampling. Some experience with Python would also be very beneficial for readers to get started on this journey of Bayesian modeling. The authors suggested a few books as possible preliminaries for their book. I feel that the readers may also benefit from reading Andrew Gelman’s book, Bayesian Data Analysis, Chapman & Hall/CRC, 3rd Edition, 2013. Of course, as the authors pointed it out, this book is not for a Bayesian Reader but a Bayesian practitioner. The book is more of an interactive experience for Bayesian practitioners by learning all the computational tools to model and to negotiate with data for a good modeling practice. On the other hand, if readers have already had experience with real-world data analysis using Python or R or other similar tools, even if this book is their first experience with Bayesian modeling and computation, readers may still learn a lot from this book. There are an abundance of figures and detailed explanations of how things are done and how the results are interpreted. Picking up these details would need some trained sensibility when dealing with real-world data, but aspiring and experienced practitioners should find all the details useful and impressive. And there are also many big picture schematic drawings to help readers connect all the details with overall concepts such as end-to-end workflows. The Figure 9.1 is a remarkable example. Overall, as Kevin Murphy pointed out in the Forward, “this is a valuable addition to the literature, which should hopefully further the adoption of Bayesian methods”. I highly recommend readers who are interested in learning Bayesian models and their applications in practice to have this book on their bookshelf.
期刊介绍:
The objective of Journal of Quality Technology is to contribute to the technical advancement of the field of quality technology by publishing papers that emphasize the practical applicability of new techniques, instructive examples of the operation of existing techniques and results of historical researches. Expository, review, and tutorial papers are also acceptable if they are written in a style suitable for practicing engineers.
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