Bayesian statistics has been around for more than 250 years now. During this time it has enjoyed as much recognition and appreciation as disdain and contempt. Through the last few decades it has gained more and more attention from people in statistics and almost all other sciences, engineering, and even outside the walls of the academic world. This revival has been possible due to theoretical and computational developments. Modern Bayesian statistics is mostly computational statistics. The necessity for exible and transparent models and a more interpretation of statistical analysis has only contributed to the trend. Here, we will adopt a pragmatic approach to Bayesian statistics and we will not care too much about other statistical paradigms and their relationship to Bayesian statistics. The aim of this book is to learn about Bayesian data analysis with the help of Python. Philosophical discussions are interesting but they have already been undertaken elsewhere in a richer way than we can discuss in these pages. We will take a modeling approach to statistics, we will learn to think in terms of probabilistic models, and apply Bayes' theorem to derive the logical consequences of our models and data. The approach will also be computational; models will be coded using PyMC3—a great library for Bayesian statistics that hides most of the mathematical details and computations from the user. Bayesian methods are theoretically grounded in probability theory and hence it's no wonder that many books about Bayesian statistics are full of mathematical formulas requiring a certain level of mathematical sophistication. Learning the mathematical foundations of statistics could certainly help you build better models and gain intuition about problems, models, and results. Nevertheless, libraries, such as PyMC3 allow us to learn and do Bayesian statistics with only a modest mathematical knowledge, as you will be able to verify by yourself throughout this book.

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＃物联网与Python 这是Packt发行的代码存储库。 它包含从头到尾完成本书所必需的所有支持项目文件。 ##说明和导航 本书随附的代码旨在帮助您进行练习，而不是代替本书本身。 出于上下文使用，该代码可能会导致无法使用的配置，并且不提供任何保修。 该代码将如下所示： class NumberInLeds: def __init__(self): self.leds = [] for i in range(9, 0, -1): led = Led(i, 10 - i) self.leds.append(led) def print_number(self, number): print("==== Turning on {0} LEDs ====".format(num

现代雷达原理第三卷，于2014年刚刚出版，完美电子档，在前两卷的基础上，讲述几例典型的雷达系统，是雷达技术综合应用的体现。

使用C ++ Cookbook的虚幻引擎4.x脚本-第二版 这是Packt发布的《 进行的代码库。 使用C ++和UE4的功能开发高质量的游戏组件并解决脚本问题 这本书是关于什么的？ 虚幻引擎4（UE4）是受欢迎且屡获殊荣的游戏引擎，可为某些最受欢迎的游戏提供支持。 一个真正强大的游戏开发工具，再没有比现在更好的时候将其用于商业和独立项目了。 本书提供了100多种食谱，展示了如何在使用虚幻引擎开发游戏时释放C ++的功能。 本书涵盖以下激动人心的功能： 如果您觉得这本书适合您，请立即获取！ 说明和导航 所有代码都组织在文件夹中。 例如，Chapter02。 该代码将如下所示： FString name = "Tim"; int32 mana = 450; FString string = FString::Printf( TEXT( "Name = %s Mana = %d

精通OpenCV 4-第三版 这是Packt发行的的代码库。 使用C ++构建计算机视觉和图像处理应用程序的综合指南 这本书是关于什么的？ 精通OpenCV，目前已是第三版，其目标是计算机视觉工程师朝着精通OpenCV迈出了第一步。 本书将数学公式保持在一个坚实但几乎没有的最低限度，为您提供了完整的项目，从构思到运行代码，针对计算机视觉领域当前的热门话题，例如人脸识别，界标检测和姿势估计以及具有深度卷积网络的数字识别。 本书涵盖以下激动人心的功能： 使用有效的OpenCV代码示例构建实际的计算机视觉问题 发现工程和维护OpenCV项目的最佳实践 探索用于复杂计算机视觉任务的算法设计方法 通过项目使用OpenCV最新API（v4.0.0） 了解Motion的3D场景重建和结构（SfM） 使用ArUco模块研究相机校准和叠加AR 如果您觉得这本书适合您，请立即获取！ 说明和导航

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The use of Python for data analysis and visualization has only increased in popularity in the last few years. The aim of this book is to develop skills to effectively approach almost any data analysis problem, and extract all of the available information. This is done by introducing a range of varying techniques and methods such as uni- and multi- variate linear regression, cluster finding, Bayesian analysis, machine learning, and time series analysis. Exploratory data analysis is a key aspect to get a sense of what can be done and to maximize the insights that are gained from the data. Additionally, emphasis is put on presentation-ready figures that are clear and easy to interpret.

pandoc-templates：一套有用的Pandoc模板和脚本，用于将markdown转换为DOCX手稿，该文档遵循William Shunn使用Pandoc的“正确手稿格式”准则