# Bayesian Statistics

**Mine Ã‡etinkaya-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.

University: | Duke University |
---|---|

Platform: | Coursera |

Start: | |

Recurrence: | flexible |

Language: | English |

Discipline: | Economics |

Attendance: | free |

Certificate: | 71.00 EUR |

Workload per week: | 5.0 h |

Tags | Baye's rule , posterior probability , prior probability , probability , probability distribution , R , statistics |

## Donate

This project is brought to you by the Network for Pluralist Economics (Netzwerk Plurale Ökonomik e.V.). It is committed to diversity and independence and is dependent on donations from people like you. Regular or one-off donations would be greatly appreciated.