Workshop: Treating missing data in bayesian models
Workshop Overview:聽The goal of this workshop is to serve as an introduction to Bayesian models and tools for analyzing missing or partially observed data. Specifically, we will cover the different types of missing data that one can encounter when working on real problems and various approaches for analyzing the incomplete data under different assumptions.聽 We will begin by considering problems where observations for some characteristics are completely missing in the original dataset. Then we will address Bayesian models for partially observed values, e.g. for censored or measurement-error contaminated values.
Participants will get access to several worked examples written in STAN, NIMBLE, and other R packages (e.g. mitools) that are often used in the analysis of data with missing values.聽
At the end of this workshop, you will be able to:
聽聽 - Understand the different kinds of assumptions one can choose from for missing data models with completely missing observations;
聽聽 - Understand the importance of incorporating appropriate uncertainty in any analysis where there are missing values;
聽聽聽- Identify different patterns of missing values for multivariate datasets and how they affect analyses;
聽聽聽- Identify different ways that data can be partially observed and the choices of assumptions for how that occurs;
聽聽聽- Fit Bayesian models in STAN and NIMBLE to data with missing values or Bayesian inference in commonly used models.
Pre-requisites:
聽聽 - An undergraduate/graduate introduction to probability;
聽聽 - Knowledge of R;
聽聽 - An introduction to Bayesian statistics and methods.
聽聽聽聽聽 路 Install R and RStudio on your computer. You can find installation instructions聽here. Please contact us (cdsi.science [at] mcgill.ca) if you are having trouble with installation.
聽聽聽聽聽 路 You need to bring your own laptop for this workshop. Contact us if you would like to attend but it's impossible for you to bring a laptop.
Location:聽HYBRID. Online via Zoom, or in-person at聽聽room 1104 (11th floor).
滨苍蝉迟谤耻肠迟辞谤:听Prof. Russell Steele, Dept. of Mathematics and Statistics, 捆绑SM社区.
Registration:
