Bayesian Statistics
date_range Débute le 21 janvier 2019
event_note Se termine le 4 mars 2019
list 5 séquences
assignment Niveau : Intermédiaire
chat_bubble_outline Langue : Anglais
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Les infos clés

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timer 25 heures de cours

En résumé

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. We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."

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Le programme

  • 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...
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Les intervenants

Mine Çetinkaya-Rundel
Associate Professor of the Practice
Department of Statistical Science

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

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Le concepteur

Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
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La plateforme

Coursera est une entreprise numérique proposant des formations 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.

Avis de la communauté
4.4 /5 Moyenne
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Le meilleur avis

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.

le 3 mars 2018
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le 3 mars 2018
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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.

le 2 mars 2018
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I really appreciated the content, and the way it was taught by Prof. Lee. His explanations were intuitive, without loss of mathematical rigour.

le 2 mars 2018
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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.

le 1 mars 2018
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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.

le 24 février 2018
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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.