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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귀하가 습득할 기술
- 5 stars45.15%
- 4 stars20.66%
- 3 stars14.54%
- 2 stars9.18%
- 1 star10.45%
베이지안 통계의 최상위 리뷰
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.
Very good introduction to Bayesian Statistics. Very interactive with Labs in Rmarkdown. Definitely requires thinking and a good math/analytic background is helpful.
This course and the others that are part of the specialization are excellent. Those of us who are beginners in Bayesian Statistics may find the material a bit confusing.
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.
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