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.
제공자:
베이지안 통계
듀크대학교이 강좌에 대하여
귀하가 습득할 기술
- Bayesian Statistics
- Bayesian Linear Regression
- Bayesian Inference
- R Programming
제공자:
듀크대학교
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.
강의 계획표 - 이 강좌에서 배울 내용
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.
검토
- 5 stars45.19%
- 4 stars20.48%
- 3 stars14.59%
- 2 stars9.21%
- 1 star10.49%
베이지안 통계의 최상위 리뷰
The section about Beta-Binomial Conjugate is taught very fast and unless the student is quite familiar with Beta and Gamma distributions, it makes it very difficult to follow the course.
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.
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.
An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed
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What background knowledge is necessary?
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