Bayesian Methods, A General Introduction下载

weixin_39820835 2019-06-25 10:00:20

一篇关于贝叶斯的介绍性文章，96年的，需要的可以看看
相关下载链接：//download.csdn.net/download/mynamehrm/2661114?utm_source=bbsseo

...全文

5 回复打赏收藏转发到动态举报

写回复

回复

切换为时间正序

请发表友善的回复…

发表回复

一篇关于贝叶斯的介绍性文章，96年的，需要的可以看看

Bayesian methods are increasingly becoming attractive to researchers in many fields. Econometrics, however, is a field in which Bayesian methods have had relatively less influence. A key reason for this absence is the lack of a suitable advanced undergraduate or graduate level textbook. Existing Bayesian books are either out-dated, and hence do not cover the computational advances that have revolutionized the field of Bayesian econometrics since the late 1980s, or do not provide the broad coverage necessary for the student interested in empirical work applying Bayesian methods. For instance, Arnold Zellner’s seminal Bayesian econometrics book (Zellner, 1971) was published in 1971. Dale Poirier’s influential book (Poirier, 1995) focuses on the methodology and statistical theory underlying Bayesian and frequentist methods, but does not discuss models used by applied economists beyond regression. Other important Bayesian books, such as Bauwens, Lubrano and Richard (1999), deal only with particular areas of econometrics (e.g. time series models). In writing this book, my aim has been to fill the gap in the existing set of Bayesian textbooks, and create a Bayesian counterpart to the many popular non-Bayesian econometric textbooks now available (e.g. Greene, 1995). That is, my aim has been to write a book that covers a wide range of models and prepares the student to undertake applied work using Bayesian methods. This book is intended to be accessible to students with no prior training in econometrics, and only a single course in mathematics (e.g. basic calculus). Students will find a previous undergraduate course in probability and statistics useful; however Appendix B offers a brief introduction to these topics for those without the prerequisite background. Throughout the book, I have tried to keep the level of mathematical sophistication reasonably low. In contrast to other Bayesian and comparable frequentist textbooks, I have included more computer-related material. Modern Bayesian econometrics relies heavily on the computer, and developing some basic programming skills is essential for the applied Bayesian. The required level of computer programming skills is not that high, but I expect that this aspect of Bayesian econometrics might be most unfamiliar to the student brought up in the world of spreadsheets and click-and-press computer packages. Accordingly, in addition to discussing computation in detail in the book itself, the website associated with the book contains MATLAB programs for performing Bayesian analysis in a wide variety of models. In general, the focus of the book is on application rather than theory. Hence, I expect that the applied economist interested in using Bayesian methods will find it more useful than the theoretical econometrician.

课程收获：1，基于ChatGPT的端到端语音聊天机器人项目实战，包括ChatGPT API后台开发、FastAPI构建语音聊天机器人后端实战、React构建语音聊天机器人前端实战等。2，企业级ChatGPT开发的三大核心内幕及案例实战，包括ChatGPT代码案例演示、企业级ChatGPT开发的核心剖析以及Models、Tools、Data在企业级ChatGPT开发中的作用及源码分析。3，ChatGPT底层架构Transformer技术及源码实现，包括Language Model底层的数学原理、Transformer架构设计、贝叶斯Bayesian Transformer数学推导、智能对话机器人中的Transformer内幕等。4，GPT内幕机制及源码实现逐行解析，包括语言模型的运行机制、GPT的可视化与Masking等工作机制、Decoder-Only模式内部运行机制以及数据在GPT模型中的流动生命周期等。5，GPT-2源码实现及GPT-3、GPT-3.5、GPT-4及GPT-5内幕解析，对GPT-2源码进行解析，探讨GPT-3，GPT-3.5、GPT-4和GPT-5的内幕机制。6，ChatGPT Plugins内幕、源码及案例实战，介绍ChatGPT Plugins的工作原理，并进行源码解析和实战演示。7，ChatGPT Prompting开发实战，包括针对迭代过程、聊天机器人和客户服务的Prompting开发实战。8，CoT及ReAct解密与实战，深入剖析Chain of Thought Reasoning、Chaining Prompts、ReAct技术原理及框架，并进行实战演示。9，Prompt本质解密及Evaluation实战与源码解析，探索Prompt的本质解密、以客户服务案例为例进行Evaluation实战，并对Evaluation for Agents和Evaluation for QA的源码进行解析。10，最火爆的大模型框架LangChain七大核心及案例剖析，包括Models、Prompts、Memory、Indexes、Callbacks等核心内容及案例剖析。11，课程总共3万行NLP/ChatGPT/LLMs项目源码逐行视频讲解。

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates……

《蒙特卡罗统计方法(第2版)(英文版)》内容简介：Introduction、Statistical Models、Likelihood Methods、Bayesian Methods、Deterministic Numerical Methods、Optimization、Integration、Comparison、Problems、Notes、Prior Distributions、Bootstrap Methods、Random Variable Generation、Introduction、Uniform Simulation、The Inverse Transform、Alternatives、Optimal Algorithms、General Transformation Methods、Accept-Reject Methods、The Fundamental Theorem of Simulation、The Accept-Reject Algorithm、Envelope Accept-Reject Methods、The Squeeze Principle、Log-Concave Densities等等。

下载资源悬赏专区

12,780

社区成员

12,308,793

社区内容

发帖

与我相关

我的任务

其他技术论坛（原bbs）

社区管理员

加入社区

近7日
近30日
至今

加载中

查看更多榜单

社区公告

暂无公告

试试用AI创作助手写篇文章吧

+ 用AI写文章