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.
提供：
ベイズ統計
デューク大学（Duke University）このコースについて
習得するスキル
- Bayesian Statistics
- Bayesian Linear Regression
- Bayesian Inference
- R Programming
提供：
デューク大学（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.
シラバス - 本コースの学習内容
About 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!
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.
Bayesian 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.
Decision 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.
Bayesian 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.
レビュー
- 5 stars45.29%
- 4 stars20.64%
- 3 stars14.70%
- 2 stars9.29%
- 1 star10.06%
ベイズ統計 からの人気レビュー
Learnt a lot. Though the subject material was hard to grasp first hand, it is good that instructor was readily available to help us through.
A bit more depth in explaining conjugacy in priors and posteriors will be very helpful. A possible way would be to have more example illustrations.
I find the teaching a bit unclear. I still don't sure I understand how to use Bayesianinference on problems I encounter in my work.
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.
よくある質問
いつ講座や課題にアクセスできるようになりますか？
この専門講座をサブスクライブすると何を行うことができるようになりますか？
学資援助はありますか？
What background knowledge is necessary?
Will I receive a transcript from Duke University for completing this course?
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