課程信息

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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."

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中級

完成時間大約為35 小時

英語（English）

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體驗 Coursera 企業版

您將獲得的技能

Bayesian Statistics
Bayesian Linear Regression
Bayesian Inference
R Programming

可靈活調整截止日期

根據您的日程表重置截止日期。

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

中級

完成時間大約為35 小時

英語（English）

對員工進行熱門技能培訓能否為您的公司帶來益處？

體驗 Coursera 企業版

授課教師

授課教師評分

3.64/5 （47 個評分）

授課大綱 - 您將從這門課程中學到什麼

內容評分78%（3,981 個評分）

週

1

週 1

完成時間為 1 小時

About the Specialization and the Course

完成時間為 1 小時

1 個視頻（總計 2 分鐘）, 4 個閱讀材料

完成時間為 6 小時

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.

完成時間為 6 小時

9 個視頻（總計 41 分鐘）, 4 個閱讀材料, 3 個測驗

9 個視頻

The Basics of Bayesian Statistics1分鐘

Conditional Probabilities and Bayes' Rule2分鐘

Bayes' Rule and Diagnostic Testing6分鐘

Bayes Updating2分鐘

Bayesian vs. frequentist definitions of probability4分鐘

Inference for a Proportion: Frequentist Approach3分鐘

Inference for a Proportion: Bayesian Approach7分鐘

Effect of Sample Size on the Posterior2分鐘

Frequentist vs. Bayesian Inference9分鐘

4 個閱讀材料

Module Learning Objectives2小時

About Lab Choices10分鐘

Week 1 Lab Instructions (RStudio)2小時

Week 1 Lab Instructions (RStudio Cloud)10分鐘

3 個練習

Week 1 Lab30分鐘

Week 1 Practice Quiz20分鐘

Week 1 Quiz30分鐘

週

2

週 2

完成時間為 7 小時

Bayesian Inference

完成時間為 7 小時

10 個視頻（總計 45 分鐘）, 3 個閱讀材料, 3 個測驗

10 個視頻

Bayesian Inference1分鐘

From the Discrete to the Continuous5分鐘

Elicitation6分鐘

Conjugacy4分鐘

Inference on a Binomial Proportion5分鐘

The Gamma-Poisson Conjugate Families6分鐘

The Normal-Normal Conjugate Families3分鐘

Non-Conjugate Priors4分鐘

Credible Intervals3分鐘

Predictive Inference4分鐘

3 個閱讀材料

Module Learning Objectives2小時

Week 2 Lab Instructions (RStudio)3小時

Week 1 Lab Instructions (RStudio Cloud)10分鐘

3 個練習

Week 2 Lab30分鐘

Week 2 Practice Quiz20分鐘

Week 2 Quiz40分鐘

週

3

週 3

完成時間為 8 小時

Decision Making

完成時間為 8 小時

14 個視頻（總計 75 分鐘）, 3 個閱讀材料, 3 個測驗

14 個視頻

Decision making53

Losses and decision making3分鐘

Working with loss functions6分鐘

Minimizing expected loss for hypothesis testing5分鐘

Posterior probabilities of hypotheses and Bayes factors6分鐘

The Normal-Gamma Conjugate Family6分鐘

Inference via Monte Carlo Sampling3分鐘

Predictive Distributions and Prior Choice5分鐘

Reference Priors7分鐘

Mixtures of Conjugate Priors and MCMC6分鐘

Hypothesis Testing: Normal Mean with Known Variance7分鐘

Comparing Two Paired Means Using Bayes' Factors6分鐘

Comparing Two Independent Means: Hypothesis Testing3分鐘

Comparing Two Independent Means: What to Report?5分鐘

3 個閱讀材料

Module Learning Objectives2小時

Week 3 Lab Instructions (RStudio)3小時

Week 3 Lab Instructions (RStudio Cloud)10分鐘

3 個練習

Week 3 Lab30分鐘

Week 3 Practice Quiz16分鐘

Week 3 Quiz40分鐘

週

4

週 4

完成時間為 8 小時

Bayesian Regression

完成時間為 8 小時

11 個視頻（總計 72 分鐘）, 3 個閱讀材料, 3 個測驗

11 個視頻

Bayesian regression1分鐘

Bayesian simple linear regression8分鐘

Checking for outliers4分鐘

Bayesian multiple regression4分鐘

Model selection criteria5分鐘

Bayesian model uncertainty7分鐘

Bayesian model averaging7分鐘

Stochastic exploration8分鐘

Priors for Bayesian model uncertainty8分鐘

R demo: crime and punishment9分鐘

Decisions under model uncertainty7分鐘

3 個閱讀材料

Module Learning Objectives2小時

Week 4 Lab Instructions (RStudio Cloud)3小時

Week 4 Lab Instructions (RStudio Cloud)10分鐘

3 個練習

Week 4 Lab22分鐘

Week 4 Practice Quiz20分鐘

Week 4 Quiz40分鐘

審閱

3.8

253 條評論

5 stars
45.19%
4 stars
20.48%
3 stars
14.59%
2 stars
9.21%
1 star
10.49%

來自贝叶斯统计的熱門評論

由 MC 提供2018年6月20日

It was a good course, though I would include more coursework and exercises in R to assist with comprehending a difficult subject. Overall, good course for something that's difficult to teach.

由 SH 提供2017年10月29日

The course is compact that I've learnt a lot of new concepts in a week of coursework. A good sampler of topics related to Bayesian Statistics.

由 TM 提供2017年3月9日

I wanted to tools for Bayesian Statistics to be as functional as the other tools available. No problem with the class. I think the material will get there for R.

由 FG 提供2016年12月15日

This is my first course on bayesian statistics, I really like it, it was step by step, and helps to clarify lots of concepts of frequentist statistic.

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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.

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