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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- Week 1 -
**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! - Week 1 -
**The Basics of Bayesian Statistics**Welcome! Over the next several weeks, we will together explore Bayesian statistics.

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 de...

- Week 2 -
**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 post... - Week 3 -
**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. - Week 4 -
**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 l... - Week 5 -
**Perspectives on Bayesian Applications**

This week consists of interviews with statisticians on how they use Bayesian statistics in their work, as well as the final project in the course. - Week 5 -
**Data Analysis Project**

In this module you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review...

**David Banks**, Professor of the Practice

Statistical Science**Colin Rundel**, Assistant Professor of the Practice

Statistical Science**Merlise A Clyde**, Professor

Department of Statistical Science

Coursera est une entreprise numérique proposant des formation en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

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5 / 5

Terminé

Alot of information, concise and clarity is awesome. Would recommend this course to anyone. And I did too. Great, professor. My only suggestion is to speak a little slower.

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5 / 5

Terminé

I really appreciated the content, and the way it was taught by Prof. Lee. His explanations were intuitive, without loss of mathematical rigour.

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4 / 5

Terminé

Herbert Lee is great at explaining the mathematics behind Bayesian statistics. However, I think the course can improve greatly by also focusing more on context and the intuition behind the mathematics. I often found that I was able to pass all quizzes, while I did not 100% understand why I was doing what I was doing.

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4 / 5

Terminé

I liked that the teacher put things into perspective and showed the connections between the different concepts. I deduct 1 star, because the additional material in rare: Meaning, you have to take notes in the lectures to solve the quizzes and to have something for looking things up. Furthermore, in a few lectures it was difficult to read what the teacher was writing, because he was wearing a shirt with a too bright color. (Sounds funny, but I mean this serious ;-) ) In summary, a great lecture and perfect introduction into the concepts. The quizzes are constructed in a way, that they encourage learning rather than frustration.

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5 / 5

Terminé

As a primer to Bayesian Statistics, this course covers the basics at a brisk pace. No time is wasted in explaining the basics of Probability theory; which I have always found, at best, to be distracting in the other similar courses I have taken. Thank you, Herbert Lee and Coursera.

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5 / 5

Terminé

The course manages very well to balance out comprehensibility and content. Professor Herbert Lee has obviously prepared the material very thoroughly and imparts the content of the course in an enjoyable fashion.

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5 / 5

Terminé

A good introduction to the concepts conveyed by revealing the equations and expressions on a whiteboard. Minimal work with data and programming - much less of this than other Coursera classes on the same topics. Also unlike other Coursera classes on the same topic, the quiz answers/hints are useful and contain the relevant equations or R commands - not merely "correct" or "you should not have chosen this answer." I found this very helpful for self learning and confirming solution approach.

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5 / 5

Terminé

Invaluable. Excellent quizzes. A few terms could have been better defined, and a few more examples wouldn't hurt, but overall excellent.

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4 / 5

Terminé

This is a great course and I have learned a lot. The teacher is extremely knowledgeable and formulates things very clearly. However, this is really a math course. For me it was hard to stay motivated because the language of the course is mathematics, the teacher juggles with the concepts that my mind was still trying to process and absorb. I was able to finish all exercises, including the honors ones, but when I finished the week 3, I had to redo it completely again and buy a book on Bayesian statistics by John Kruschke which helped me immensely to rethink the basic concepts again. This course could be excellent if it included more reiterations of concepts, was explained in more general language, the pace was slower and most importantly included more practical applications. The typical statistical examples of coin flipping are fun, but too abstract. In the end, I want to know how I can apply Bayesian statistics. A lot of knowledge of mathematics was assumed and I had to look up a lot of concepts myself. The derivations sometimes also went too quick and supplementary materials were quite dense. I think this course is a perfect refresher course for someone who has mathematical background and has taken a Bayesian statistics course some time ago. But for the beginner with some mathematical background (I am familiar with the frequentist statistics, machine learning, calculus) it was too much of a challenge. If it were not a Coursera course, where I can rewind endlessly and work at my own pace, but a regular university course, there will be p=.9 that I would drop out, while my prior for dropping out would be p=.05

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4 / 5

Terminé

This is a good course, I've learn a lot about Bayesian statistics with very little prior knowledge about this subject and even about statistics and probability in general.

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