Bayesian Statistics

Niveau: débutant
Mine Çetinkaya-Rundel; David Banks; Colin Rundel; Merlise A Clyde

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.

Université: Duke University
Plateforme: Coursera
Date de début:
Fréquence: flexible
Langue: English
Discipline: Economics
Modalité de suivi: gratuit
Certificat: 71,00 EUR
Charge de travail hebdomadaire: 5,0 h
Tags Baye's rule , posterior probability , prior probability , probability , probability distribution , R , statistics

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Ce projet est supervisé par des membres du réseau international pour une science économique pluraliste, dans la sphère germanophone (Netzwerk Plurale Ökonomik e.V.) et dans la sphère francophone (Rethinking Economics Switzerland / Rethinking Economics Belgium / PEPS-Économie France). Nous sommes fortement attachés à notre indépendance et à notre diversité et sommes donc dépendants de donations de personnes telles que vous. Des dons réguliers ou ponctuels sont les bienvenus !