Bayesian Inference with MCMC

所在平台: Coursera

课程主页: https://www.coursera.org/learn/mcmc

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课程简介

课程名称：基于MCMC的贝叶斯推断 课程概述： 本课程旨在介绍用于贝叶斯建模和推断的马尔可夫链蒙特卡罗方法（MCMC）。学员将从了解蒙特卡罗方法的基础知识开始，并通过使用Python的实际示例来加深对这些算法工作的理解。该课程是一个三门课程专业化中的第二门课程。课程中将贯穿使用Python和Jupyter笔记本，通过PyMC3来进行贝叶斯建模。课程网站地址为：https://sjster.github.io/introduction_to_computational_statistics/docs/index.html，课程笔记本可通过网站上的指示进行下载，链接为：https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html。授课教师为Dr. Srijith Rajamohan。 课程大纲： 1. 模型性能主题 描述：本模块概述与评估模型质量相关的主题。虽然对于具有机器学习背景的人来说，其中一些指标可能是熟悉的，但目标是提升对信息理论根基概念的认识。课程网站为：https://sjster.github.io/introduction_to_computational_statistics/docs/Production/BayesianInference.html，笔记本下载及运行说明见：https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html。 2. MCMC的Metropolis算法 描述：本模块作为马尔可夫链蒙特卡罗方法的温和介绍，呈现马尔可夫链的基本思想及其在从分布中采样中的作用。介绍并在Python中实现Metropolis及Metropolis-Hastings算法，以帮助说明它们的细节。课程网站为：https://sjster.github.io/introduction_to_computational_statistics/docs/Production/MonteCarlo.html，笔记本下载及运行说明见：https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html。 3. Gibbs采样和哈密顿蒙特卡罗算法 描述：本模块为第二模块的延续，介绍了用于推断分布的Gibbs采样和哈密顿蒙特卡罗（HMC）算法。详细阐述Gibbs采样算法，由于HMC算法的复杂性，它的介绍将进行更高层次的处理。最后，介绍一些MCMC算法的属性，为第三门课程使用流行的概率框架PyMC3奠定基础。课程网站为：https://sjster.github.io/introduction_to_computational_statistics/docs/Production/MonteCarlo.html#gibbs-sampling，笔记本下载及运行说明见：https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html。

课程大纲

Name:Topics in Model Performance

Description:This module gives an overview of topics related to assessing the quality of models. While some of these metrics may be familiar to those with a Machine Learning background, the goal is to bring awareness to the concepts rooted in Information Theory. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/BayesianInference.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

Name:The Metropolis Algorithms for MCMC

Description:This module serves as a gentle introduction to Markov-Chain Monte Carlo methods. The general idea behind Markov chains are presented along with their role in sampling from distributions. The Metropolis and Metropolis-Hastings algorithms are introduced and implemented in Python to help illustrate their details. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/MonteCarlo.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

Name:Gibbs Sampling and Hamiltonian Monte Carlo Algorithms

Description:This module is a continuation of module 2 and introduces Gibbs sampling and the Hamiltonian Monte Carlo (HMC) algorithms for inferring distributions. The Gibbs sampler algorithm is illustrated in detail, while the HMC receives a more high-level treatment due to the complexity of the algorithm. Finally, some of the properties of MCMC algorithms are presented to set the stage for Course 3 which uses the popular probabilistic framework PyMC3. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/MonteCarlo.html#gibbs-sampling. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

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