(c) 2016 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. You can also view it here. Use this downloaded Jupyter notebook to fill out your responses.
Imagine you have a validated, tidy data set, and you want to compare two statistical models. Write down a work flow on how you would compare the two models.
List pros and cons of performing parameter estimation/model selection by optimization/Laplace approximation versus parallel tempering Markov chain Monte Carlo.
If we are trying to assess the quality of a model, why must we have at least one other model to compare it to?