(c) 2017 Justin Bois. 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 tutorial exercise was generated from an Jupyter notebook. You can download the notebook here. Use this downloaded Jupyter notebook to fill out your responses.
List pros and cons of performing parameter estimation/model comparison by optimization/Laplace approximation versus MCMC/WAIC.
If you are trying to assess the quality of a model, why must you have at least one other model to compare it to?
Give an example of a situation where it is ok to throw out data you acquired.
If you were to summarize the posterior with a MAP/error bar using optimization (not MCMC), would it be easier to use the Cauchy method of dealing with outliers or the good/bad data model? Why?