Overview
Description
Students will learn the basics of designing and carrying out Bayesian analyses and interpreting and communicating results through hands-on experience with real data and computer simulation methods.
Requirements
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring
Terms
spring
Attributes
Standard
Learning Outcomes
- summarize the relative strengths of Bayesian and frequentist methods.
- formulate models to address real world problems.
- formulate and implement Bayesian statistical models to analyze data.
- write, run, and diagnose posterior sampling algorithms for basic models.
- implement standard software (i.e., R, Stan) to conduct a Bayesian analysis based on advanced models.
- critically evaluate Bayesian statistical methodologies.