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.

Voir plus

- 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.

Lire les avis

Trier par :

5 / 5

Terminé

Thank you so much, Herbert Lee. I really like the way you explain everything clearly and how you organizes the contents. I recommend this course for my friends.

0

4 / 5

Terminé

This course covers most of the basics in a very good manner. I personally feel, the last week chapters especially regression do not connect the dots between the foundation that was laid and the resources provided were also not very helpful to fill that gap. For e.g I wanted to understand regression from the bayesian context, the session mostly focused on how to do regression in R and the not the internals of how to understand the mechanics behind from the bayesian stand. I will be helpful to introduce some content that helps the user to move from univariate normal distribution to multivariate normal distribution and explains some intuition behind them.

0

5 / 5

Terminé

A very well-organized course. Not a hard one, but one with sufficient quizzes to make sure you understand every concept by solving problems.Another thing I like about this course, is that I had to actively write a lot of codes in Python and Matlab when doing the exercises(due to my familiarity with these two), although the course teaches a little bit R and Excel programming. This is a very effective way of teaching.

0

5 / 5

Terminé

Very interesting course.For me the most interesting and important themes are about priors:1) conjugated priors2) effective prior size3) how to choose a prior4) non-informative priors5) improper priors6) Jeffreys priors

0

5 / 5

Terminé

This is first time exposure to bayesian statistics and I must say it has given me a different perspective to analyzing data especially when dealing with unpredictable data or unknown data.

0

5 / 5

Terminé

A great introduction to bayesian statistics. I warmly recommend this course to those already familiar with the frequentist approach and willing to expand their knowledge.

0

5 / 5

Terminé

I strongly recommend this course to those who are interested in learning theoretical concepts that build Machine Learning statistics especially Bayesian. The course content was well organized and the professor presented the concepts in a very engaging way. Relevant and appropriate examples and in-video quizzes were very helpful in understanding the theory.

0

5 / 5

Terminé

I've learned how to process data and analyze data from studies, that's a wonderful ability I think everybody should try to learn in order to not get manipulated by the media. Thanks for this course!

0

5 / 5

Terminé

Prof Lee derived the formulas in an upbeat way, which helped me learn. I'd suggest putting the actual lectures into pdf for later reference, like is done for supplementary material. Homework assignments were challenging and educational. You might suggest a review of prob distributions as pre-requisite.

0

5 / 5

Terminé

Excellent course. For such broad discipline I'm sure it was difficult to choose most important material to fit 4-week course, yet professor did it perfectly. I'd love to see this course in Python, but I guess I can't have everything ;) I'd also love see some examples of using probabilistic programming packages, like Stan or PyMC3 in more real-life problems - I would give 6/5 stars for it!

0

vous pourriez aussi être intéressé par...

MESSAGE_PLACEHOLDER