Tutorial 10: exercise

(c) 2018 Justin Bois. With the exception of pasted graphics, where the source is noted, this work is licensed under a Creative Commons Attribution License CC-BY 4.0. All code contained herein is licensed under an MIT license.

This document was prepared at Caltech with financial support from the Donna and Benjamin M. Rosen Bioengineering Center.

This tutorial exercise was generated from an Jupyter notebook. You can download the notebook here. Use this downloaded Jupyter notebook to fill out your responses.

Exercise 1

Give an example of a situation where it is ok to throw out data you acquired.

Exercise 2

If you were to summarize the posterior with a MAP/error bar using optimization (not MCMC), would it be easier to use the Student-t method of dealing with outliers or the good/bad data model? Why?

Exercise 3

Describe in words the generative model behind the use of all three outlier methods discussed in the tutorial (median plugin estimate, Student-t likelihood, good-bad data model).