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.
Bayessche StatistikDuke University
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- 4 stars20,48 %
- 3 stars14,59 %
- 2 stars9,21 %
- 1 star10,49 %
Top-Bewertungen von BAYESSCHE STATISTIK
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?
Will I receive a transcript from Duke University for completing this course?
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