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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/bayesian
课程评论：没有评论
课程名称：贝叶斯统计 概述：本课程介绍贝叶斯统计，讲述如何根据证据的累积来更新对参数或假设的推断。您将学习使用贝叶斯法则将先验概率转化为后验概率，并了解贝叶斯范式的理论基础和视角。课程将应用贝叶斯方法解决多个实际问题，展示从问题框架到构建模型、引导先验概率，再到在R（免费统计软件）中实现最终后验分布的端到端贝叶斯分析。此外，课程将介绍可信区间、均值和比例的贝叶斯比较、贝叶斯回归及使用多种模型的推断，还将讨论贝叶斯预测。 我们假设本课程的学习者具有与本专门化前面三门课程相当的背景知识，包括《概率与数据导论》、《推断统计》和《线性回归与建模》。 课程大纲： 第一部分：贝叶斯统计基础 描述：欢迎！在接下来的几周里，我们将共同探索贝叶斯统计。在本模块中，我们将处理条件概率，即给定事件A的事件B的概率。条件概率在医疗决策中非常重要。到本周结束时，您将能够使用贝叶斯法则解决问题，并更新先验概率。 第二部分：贝叶斯推断 描述：在这一周，我们将讨论贝叶斯法则的连续版本，并展示如何在共轭族中使用它，同时讨论可信区间。到本周结束时，您将能够理解和定义先验、似然和后验概率的概念，并识别它们之间的关系。 第三部分：决策制定 描述：在本模块中，我们将讨论贝叶斯决策、假设检验和贝叶斯检验。到本周结束时，您将能够基于贝叶斯统计做出最佳决策，并使用贝叶斯因子比较多个假设。 第四部分：贝叶斯回归 描述：本周，我们将关注贝叶斯线性回归和模型加权，这使您能够使用多个模型进行推断和预测。到本周结束时，您将能够实施贝叶斯模型加权，解释贝叶斯多元线性回归，并理解其与频率统计线性回归方法的关系。 第五部分：数据分析项目 描述：在本模块中，您将使用提供的数据集完成并报告一个数据分析问题。请阅读背景信息，查看报告模板（可通过“课程项目信息”中的链接下载），然后完成同行评审作业。
Part: 1
Title:The Basics of Bayesian Statistics
Description:
Welcome! Over the next several weeks, we will together explore Bayesian statistics.
In this module, we will work with conditional probabilities, which is the probability of event B given event A. Conditional probabilities are very important in medical decisions. By the end of the week, you will be able to solve problems using Bayes' rule, and update prior probabilities.
Please use the learning objectives and practice quiz to help you learn about Bayes' Rule, and apply what you have learned in the lab and on the quiz.
Part: 2
Title:Bayesian Inference
Description:In this week, we will discuss the continuous version of Bayes' rule and show you how to use it in a conjugate family, and discuss credible intervals. By the end of this week, you will be able to understand and define the concepts of prior, likelihood, and posterior probability and identify how they relate to one another.
Part: 3
Title:Decision Making
Description:In this module, we will discuss Bayesian decision making, hypothesis testing, and Bayesian testing. By the end of this week, you will be able to make optimal decisions based on Bayesian statistics and compare multiple hypotheses using Bayes Factors.
Part: 4
Title:Bayesian Regression
Description:This week, we will look at Bayesian linear regressions and model averaging, which allows you to make inferences and predictions using several models. By the end of this week, you will be able to implement Bayesian model averaging, interpret Bayesian multiple linear regression and understand its relationship to the frequentist linear regression approach.
Part: 5
Title:Data Analysis Project
Description:In this module you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review assignment.
This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction. We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."
