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."
Ce cours fait partie de la Spécialisation Statistics with R
Statistiques bayésiennes
proposé par
À propos de ce cours
3.9
521 notes
Compétences que vous acquerrez
Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
11 heure pour terminer
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!
1 vidéo (Total 2 min), 4 lectures
1 vidéo
4 lectures
About Statistics with R Specialization10 min
About Bayesian Statistics10 min
Pre-requisite Knowledge10 min
Special Thanks2 min
6 heures pour terminer
The Basics of Bayesian Statistics
<p>Welcome! Over the next several weeks, we will together explore Bayesian statistics. <p>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 decisions. By the end of the week, you will be able to solve problems using Bayes' rule, and update prior probabilities.</p><p>Please use the learning objectives and practice quiz to help you learn about Bayes' Rule, and apply what you have learned in the lab and on the quiz.
9 vidéos (Total 41 min), 4 lectures, 3 quiz
9 vidéos
Conditional Probabilities and Bayes' Rule2 min
Bayes' Rule and Diagnostic Testing6 min
Bayes Updating2 min
Bayesian vs. frequentist definitions of probability4 min
Inference for a Proportion: Frequentist Approach3 min
Inference for a Proportion: Bayesian Approach7 min
Effect of Sample Size on the Posterior2 min
Frequentist vs. Bayesian Inference9 min
4 lectures
Module Learning Objectivess
About Lab Choices10 min
Week 1 Lab Instructions (RStudio)s
Week 1 Lab Instructions (RStudio Cloud)10 min
3 exercices pour s'entraîner
Week 1 Lab12 min
Week 1 Practice Quiz20 min
Week 1 Quiz20 min
Semaine
27 heures pour terminer
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 posterior probability and identify how they relate to one another.
10 vidéos (Total 45 min), 3 lectures, 3 quiz
10 vidéos
Bayesian Inference1 min
From the Discrete to the Continuous5 min
Elicitation6 min
Conjugacy4 min
Inference on a Binomial Proportion5 min
The Gamma-Poisson Conjugate Families6 min
The Normal-Normal Conjugate Families3 min
Non-Conjugate Priors4 min
Credible Intervals3 min
Predictive Inference4 min
3 lectures
Module Learning Objectivess
Week 2 Lab Instructions (RStudio)s
Week 1 Lab Instructions (RStudio Cloud)10 min
3 exercices pour s'entraîner
Week 2 Lab28 min
Week 2 Practice Quiz20 min
Week 2 Quiz40 min
Semaine
38 heures pour terminer
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.
14 vidéos (Total 75 min), 3 lectures, 3 quiz
14 vidéos
Losses and decision making3 min
Working with loss functions6 min
Minimizing expected loss for hypothesis testing5 min
Posterior probabilities of hypotheses and Bayes factors6 min
The Normal-Gamma Conjugate Family6 min
Inference via Monte Carlo Sampling3 min
Predictive Distributions and Prior Choice5 min
Reference Priors7 min
Mixtures of Conjugate Priors and MCMC6 min
Hypothesis Testing: Normal Mean with Known Variance7 min
Comparing Two Paired Means Using Bayes' Factors6 min
Comparing Two Independent Means: Hypothesis Testing3 min
Comparing Two Independent Means: What to Report?5 min
3 lectures
Module Learning Objectivess
Week 3 Lab Instructions (RStudio)s
Week 3 Lab Instructions (RStudio Cloud)10 min
3 exercices pour s'entraîner
Week 3 Lab22 min
Week 3 Practice Quiz16 min
Week 3 Quiz40 min
Semaine
48 heures pour terminer
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 linear regression and understand its relationship to the frequentist linear regression approach.
11 vidéos (Total 72 min), 3 lectures, 3 quiz
11 vidéos
Bayesian regression1 min
Bayesian simple linear regression8 min
Checking for outliers4 min
Bayesian multiple regression4 min
Model selection criteria5 min
Bayesian model uncertainty7 min
Bayesian model averaging7 min
Stochastic exploration8 min
Priors for Bayesian model uncertainty8 min
R demo: crime and punishment9 min
Decisions under model uncertainty7 min
3 lectures
Module Learning Objectivess
Week 4 Lab Instructions (RStudio Cloud)s
Week 4 Lab Instructions (RStudio Cloud)10 min
3 exercices pour s'entraîner
Week 4 Lab22 min
Week 4 Practice Quiz20 min
Week 4 Quiz40 min
3.9
155 avis25%
a commencé une nouvelle carrière après avoir terminé ces cours
16%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.
I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.
Enseignants
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
David Banks
Professor of the PracticeStatistical Science
Colin Rundel
Assistant Professor of the PracticeStatistical Science
Merlise A Clyde
ProfessorDepartment of Statistical Science
À propos de Université Duke
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.
À propos de la Spécialisation Statistics with R
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
Quelle est la politique de remboursement ?
Une aide financière est-elle possible ?
What background knowledge is necessary?
We assume you have knowledge equivalent to the prior courses in this specialization.
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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