Introduction to PyMC3 for Bayesian Modeling and Inference

所在平台: Coursera

课程主页: https://www.coursera.org/learn/introduction-to-pymc3

课程评论：没有评论

第一个写评论        关注课程

课程简介

课程名称：PyMC3 贝叶斯建模与推断入门 概述：本课程旨在介绍 PyMC3 用于贝叶斯建模与推断的基础知识。学员将学习 PyMC3 的基本概念，并掌握如何针对各种问题进行可扩展的推断。该课程是三门课程专业化中的最后一门，课程中将使用 Python 和 Jupyter 笔记本来演示和执行与 PyMC3 相关的贝叶斯建模。课程网站为 https://sjster.github.io/introduction_to_computational_statistics/docs/index.html，课程笔记本可在 https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html 下载。 讲师：Dr. Srijith Rajamohan 课程大纲： 1. **PyMC3 入门 - 第一部分** - 描述：本模块介绍 PyMC3 概率编程框架的基础知识，涵盖建模相关概念和 PyMC3 语法，同时介绍与 PyMC3 集成的可视化库 ArViz。课程网站：https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html，下载和运行笔记本的说明见 https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html。 2. **PyMC3 入门 - 第二部分** - 描述：本模块将教授使用 PyMC3 解决回归和分类问题的基本知识，探讨如何处理数据中的异常值并创建层次模型。最后，通过案例研究来应用模块 1 和 2 中所学的内容。课程网站：https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#linear-regression-again，下载和运行笔记本的说明见 https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html。 3. **PyMC3 中的指标** - 描述：本模块介绍各种测量标准和指标，以评估使用 PyMC3 推断的解决方案质量。通过实际示例说明如何在 PyMC3 中使用各种方法和可视化，最后简要概述如何调试 PyMC3 算法。课程网站：https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#mcmc-metrics，下载和运行笔记本的说明见 https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html。 4. **使用 PyMC3 建模 COVID-19 病例** - 描述：这是一个未评分的最终项目。我们将利用在本课程中学到的知识，使用 SIR 模型对 COVID-19 的疾病动态进行建模。利用真实数据，目标是推断 COVID-19 SIR 模型的参数。

课程大纲

Name:Introduction to PyMC3 - Part 1

Description:This module serves as an introduction to the PyMC3 framework for probabilistic programming. It introduces some of the concepts related to modeling and the PyMC3 syntax. The visualization library ArViz, that is integrated into PyMC3, will also be introduced. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

Name:Introduction to PyMC3 - Part 2

Description:This module will teach the basics of using PyMC3 to solve regression and classification problems using PyMC3. It will also show how to deal with outliers in your data and create hierarchical models. Finally, a case study is presented to help apply everything that was learned in Module 1 and 2. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#linear-regression-again. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

Name:Metrics in PyMC3

Description:This module introduces various measures and metrics to assess the quality of the solutions inferred using PyMC3. Hands-on examples are used to illustrate how various methods and visualizations can be used in PyMC3. Finally, a brief overview of how to debug PyMC3 algorithms is provided. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#mcmc-metrics. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

Name:Modeling of COVID-19 cases using PyMC3

Description:This is an ungraded final project. We will utilize everything that has been learned in this course to model the disease dynamics of COVID-19 using a SIR model. Utilizing real-life data, the goal would be to infer the parameters of the SIR model for COVID-19.

课程评论(0条)