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Bayesian Regression and Model Selection

Bayesian Regression and Model Selection

本课程是 Applied Bayesian Data Analysis 专项课程的一部分

位教师：Konstantinos Pelechrinis

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Implement variational inference for scalable Bayesian analysis and determine when to prefer VI over MCMC methods.
Apply Gaussian Process Regression and Dirichlet Processes for flexible non-parametric modeling solutions.
Execute complete Bayesian workflows using PyMC3 from model specification through validation and diagnostics.
Build decision-theoretic models using loss functions for applications in sports analytics, healthcare, and business decision-making.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Applied Bayesian Data Analysis 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

Master Bayesian modeling through Bayesian linear regression, generalized linear models, hierarchical models and model selection. This course will deepen your understanding of modeling techniques and the importance of the prior when contrasted with traditional frequentist modeling approaches. You will understand the benefits of hierarchical models and how they automatically identify the right amount of pooling between data to provide a balance between the complete and no pooling approaches. You will learn how to apply posterior predictive checks for model selection and understand the Occam’s razor principle. This course combines theoretical modeling foundations with hands-on implementations.

单元详情

Welcome to Bayesian Regression and Model Selection! In this module, we will introduce the Bayesian linear regression. We will see how we can place priors on the coefficients of the models and what we can learn from their posteriors. We will also learn how to define and infer the posteriors of a Bayesian linear regression with pymc.

涵盖的内容

5个视频7篇阅读材料5个作业1个非评分实验室

5个视频总计18分钟

Bayesian Models4分钟
Bayesian Linear Regression3分钟
Bayesian Linear Regression in pymc4分钟
The choice of prior4分钟
Multiple predictors and interactions3分钟

7篇阅读材料总计90分钟

Course Overview10分钟
Technical and Accessibility Support5分钟
A brief review of modeling15分钟
Other Bayesian Programming Tools10分钟
Bayesian-vs-Frequentist Linear Regression30分钟
Module Wrap-Up10分钟
Recommended Learning Resources10分钟

5个作业总计85分钟

Bayesian Linear Regression10分钟
Lab Check-in: Bayesian linear regression in pymc5分钟
The effect and importance of prior10分钟
Test Yourself: Bayesian Regression - Simple and Multiple Linear Models30分钟
Let's Practice: Bayesian Regression - Simple and Multiple Linear Models30分钟

1个非评分实验室总计60分钟

Bayesian Linear Regression in pymc60分钟

In this module, we will see how hierarchical models make it easy to deal with categorical data, especially when these data are nested. We will see how they automatically identify the right amount of pooling between data to provide a balance between the complete and no pooling approaches.

涵盖的内容

5个视频1篇阅读材料5个作业1个非评分实验室

5个视频总计24分钟

Hierarchical Models4分钟
The Radon Model2分钟
Complete, No, and, Partial Pooling11分钟
Hierarchical Models3分钟
Group-level information3分钟

1篇阅读材料总计4分钟

Module Wrap-Up4分钟

5个作业总计89分钟

Approaches to Pooling12分钟
Lab Check-in: BHM example at pymc5分钟
Hierarchical models12分钟
Test Yourself: Hierarchical Bayesian Models30分钟
Let's Practice: Hierarchical Bayesian Models30分钟

1个非评分实验室总计120分钟

BHM example at pymc120分钟

In this module, we will extend the Bayesian linear regression to be able to deal with binary (categorical) and count data. We will see the Bernoulli likelihood for the Bayesian logistic regression and how we can extend it to more than two categories through the categorical likelihood. Finally, we will see the Bayesian Poisson regression (and other options) for count data.

涵盖的内容

3个视频5篇阅读材料4个作业3个非评分实验室

3个视频总计10分钟

Bayesian Logistic Regression4分钟
Poisson regression3分钟
Poisson regression example2分钟

5篇阅读材料总计72分钟

Modeling Binary and Count Data with Bayesian GLMs20分钟
Binary Data Example18分钟
Modeling Multiclass Data with Bayesian Classification14分钟
Other Distributions for Count Data15分钟
Module Wrap-Up5分钟

4个作业总计82分钟

Binary and categorical data12分钟
Count data10分钟
Test Yourself: Bayesian Logistic Regression and Generalized Linear Models (GLMs)30分钟
Let's Practice: Bayesian Logistic Regression and Generalized Linear Models (GLMs)30分钟

3个非评分实验室总计180分钟

Logistic Rainfall60分钟
Modeling Multiclass Data60分钟
Poisson Bike Trips60分钟

In this module, we will see the basic notions behind model selection and the philosophical and practical differences between frequentists and Bayesians on the topic. We will understand the difference between the posterior distribution of the model parameters and the posterior predictive distributions. The latter will lead us to the ideas of posterior predictive checks and model coverage.