À propos de ce cours
3.9
465 notes
143 avis
Spécialisation
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100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire

Niveau intermédiaire

Heures pour terminer

Approx. 29 heures pour terminer

Recommandé : 5 weeks of study, 5-7 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais

Compétences que vous acquerrez

Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming
Spécialisation
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire

Niveau intermédiaire

Heures pour terminer

Approx. 29 heures pour terminer

Recommandé : 5 weeks of study, 5-7 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
Heures pour terminer
1 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!...
Reading
1 video (Total 2 min), 4 lectures
Reading4 lectures
About Statistics with R Specialization10 min
About Bayesian Statistics10 min
Pre-requisite Knowledge10 min
Special Thanks2 min
Heures pour terminer
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. ...
Reading
9 videos (Total 41 min), 2 lectures, 3 quiz
Video9 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
Reading2 lectures
Module Learning Objectives min
Week 1 Lab Instructions min
Quiz3 exercices pour s'entraîner
Week 1 Lab12 min
Week 1 Practice Quiz20 min
Week 1 Quiz20 min
Semaine
2
Heures pour terminer
7 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....
Reading
10 videos (Total 45 min), 2 lectures, 3 quiz
Video10 vidéos
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
Reading2 lectures
Module Learning Objectives min
Week 2 Lab Instructions min
Quiz3 exercices pour s'entraîner
Week 2 Lab28 min
Week 2 Practice Quiz20 min
Week 2 Quiz40 min
Semaine
3
Heures pour terminer
8 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. ...
Reading
14 videos (Total 75 min), 2 lectures, 3 quiz
Video14 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
Reading2 lectures
Module Learning Objectives min
Week 3 Lab Instructions min
Quiz3 exercices pour s'entraîner
Week 3 Lab22 min
Week 3 Practice Quiz16 min
Week 3 Quiz40 min
Semaine
4
Heures pour terminer
8 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. ...
Reading
11 videos (Total 72 min), 2 lectures, 3 quiz
Video11 vidéos
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
Reading2 lectures
Module Learning Objectives min
Week 4 Lab Instructions min
Quiz3 exercices pour s'entraîner
Week 4 Lab22 min
Week 4 Practice Quiz20 min
Week 4 Quiz40 min
3.9
143 avisChevron Right
Orientation de carrière

17%

a commencé une nouvelle carrière après avoir terminé ces cours
Avantage de carrière

83%

a bénéficié d'un avantage concret dans sa carrière grâce à ce cours

Meilleurs avis

par RRSep 21st 2017

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.

par GHApr 10th 2018

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

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Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science
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David Banks

Professor of the Practice
Statistical Science
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Colin Rundel

Assistant Professor of the Practice
Statistical Science
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Merlise A Clyde

Professor
Department of Statistical Science

À propos de Duke University

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....
Statistics with R

Foire Aux Questions

  • 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.

  • 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.

  • We assume you have knowledge equivalent to the prior courses in this specialization.

  • 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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