课程概述
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."
热门审阅
AA
Aug 24, 2017
An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed
CH
Oct 29, 2017
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.
筛选依据：
251 - Bayesian Statistics 的 255 个评论（共 255 个）
创建者 Meta G
•Sep 5, 2023
There is almost no one taking this course so getting the peer review can take forever
创建者 Ashish C
•Aug 29, 2019
The quality of teaching was drastically down as compared to other courses.
创建者 Jeffrey W
•Jun 2, 2018
Unclear information, too vague, incomplete presentation of ideas.
创建者 Jose C G
•Dec 5, 2022
It is a pity that the course is for R
创建者 Shubham J
•Sep 14, 2019
becomes too much confusing at times.