提供方
课程信息
3.9
444 个评分
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."
您将获得的技能
Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming
教学大纲 - 您将从这门课程中学到什么
Week
1About the Specialization and the Course
This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Bayesian Statistics. Please take several minutes read this information. Thanks for joining us in this course!
1 个视频（共 2 分钟）, 4 个阅读材料
4 个阅读材料
About Statistics with R Specialization10分钟
About Bayesian Statistics10分钟
Pre-requisite Knowledge10分钟
Special Thanks2分钟
The Basics of Bayesian Statistics
<p>Welcome! Over the next several weeks, we will together explore Bayesian statistics. <p>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.</p><p>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.
9 个视频（共 41 分钟）, 2 个阅读材料, 3 个测验
9 个视频
Conditional Probabilities and Bayes' Rule2分钟
Bayes' Rule and Diagnostic Testing6分钟
Bayes Updating2分钟
Bayesian vs. frequentist definitions of probability4分钟
Inference for a Proportion: Frequentist Approach3分钟
Inference for a Proportion: Bayesian Approach7分钟
Effect of Sample Size on the Posterior2分钟
Frequentist vs. Bayesian Inference9分钟
2 个阅读材料
Module Learning Objectives分钟
Week 1 Lab Instructions分钟
3 个练习
Week 1 Lab12分钟
Week 1 Practice Quiz20分钟
Week 1 Quiz20分钟
Week
2Bayesian Inference
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.
10 个视频（共 45 分钟）, 2 个阅读材料, 3 个测验
10 个视频
From the Discrete to the Continuous5分钟
Elicitation6分钟
Conjugacy4分钟
Inference on a Binomial Proportion5分钟
The Gamma-Poisson Conjugate Families6分钟
The Normal-Normal Conjugate Families3分钟
Non-Conjugate Priors4分钟
Credible Intervals3分钟
Predictive Inference4分钟
2 个阅读材料
Module Learning Objectives分钟
Week 2 Lab Instructions分钟
3 个练习
Week 2 Lab28分钟
Week 2 Practice Quiz20分钟
Week 2 Quiz40分钟
Week
3Decision Making
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.
14 个视频（共 75 分钟）, 2 个阅读材料, 3 个测验
14 个视频
Losses and decision making3分钟
Working with loss functions6分钟
Minimizing expected loss for hypothesis testing5分钟
Posterior probabilities of hypotheses and Bayes factors6分钟
The Normal-Gamma Conjugate Family6分钟
Inference via Monte Carlo Sampling3分钟
Predictive Distributions and Prior Choice5分钟
Reference Priors7分钟
Mixtures of Conjugate Priors and MCMC6分钟
Hypothesis Testing: Normal Mean with Known Variance7分钟
Comparing Two Paired Means Using Bayes' Factors6分钟
Comparing Two Independent Means: Hypothesis Testing3分钟
Comparing Two Independent Means: What to Report?5分钟
2 个阅读材料
Module Learning Objectives分钟
Week 3 Lab Instructions分钟
3 个练习
Week 3 Lab22分钟
Week 3 Practice Quiz16分钟
Week 3 Quiz40分钟
Week
4Bayesian Regression
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.
11 个视频（共 72 分钟）, 2 个阅读材料, 3 个测验
11 个视频
Bayesian simple linear regression8分钟
Checking for outliers4分钟
Bayesian multiple regression4分钟
Model selection criteria5分钟
Bayesian model uncertainty7分钟
Bayesian model averaging7分钟
Stochastic exploration8分钟
Priors for Bayesian model uncertainty8分钟
R demo: crime and punishment9分钟
Decisions under model uncertainty7分钟
2 个阅读材料
Module Learning Objectives分钟
Week 4 Lab Instructions分钟
3 个练习
Week 4 Lab22分钟
Week 4 Practice Quiz20分钟
Week 4 Quiz40分钟
3.9
17%
83%
热门审阅
创建者 RR•Sep 21st 2017
Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.
创建者 GH•Apr 10th 2018
I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.
讲师
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
David Banks
Professor of the PracticeStatistical Science
Colin Rundel
Assistant Professor of the PracticeStatistical Science
Merlise A Clyde
ProfessorDepartment of Statistical Science
关于 Duke University
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
关于 Statistics with R 专项课程
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
常见问题
我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
What background knowledge is necessary?
We assume you have knowledge equivalent to the prior courses in this specialization.
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
还有其他问题吗？请访问 学生帮助中心。
