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.
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Statistiques bayésiennes
Université DukeÀ propos de ce cours
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Essayez Coursera pour les affairesCompétences que vous acquerrez
- Bayesian Statistics
- Bayesian Linear Regression
- Bayesian Inference
- R Programming
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Programme de cours : ce que vous apprendrez dans ce cours
About the Specialization and the Course
The Basics of Bayesian Statistics
Bayesian Inference
Decision Making
Bayesian Regression
Avis
- 5 stars45,03 %
- 4 stars20,61 %
- 3 stars14,63 %
- 2 stars9,16 %
- 1 star10,55 %
Meilleurs avis pour STATISTIQUES BAYÉSIENNES
Week 3 was too much information too soon, but week 4 was great again like the other courses in this specialisation. Learned so much, thanks!
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.
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.
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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