Week 8 Lab - Markov Chain Monte Carlo with Tide Gauge Data

This repository contains the lab notebook for Week 8 of BEE 6940, Climate Risk Analysis.

If enrolled in the class, a PDF of the completed notebook, with all cells evaluated, should be submitted to Gradescope no later than Monday, March 20, at 1:00pm. 10% will be deducted for each day that the notebook is late.

Prerequisites

1. Install Julia before beginning this lab. This notebook was developed with version 1.8.2, but any 1.8.x should work (there could be some issues with other versions, depending on what's changed).
2. If necessary, install git and create a GitHub account.
3. Clone the repository. I recommend doing this in a dedicated BEE6940/ folder, which can also house homework assignment repositories and lecture notes. You can clone directly into the BEE6940/ folder. For Windows (or from another graphical interface), just create a BEE6940 folder, then a labs folder inside of that, then clone into that folder. Or to clone into a BEE6940/labs folder, from a command prompt:

   cd BEE4750/
   mkdir labs
   cd labs/
   git clone https://github.com/ClimateRiskAnalysis/lab08.git

Opening The Notebook

1. To interact (view and run) the notebook, there are two options:

• Install an integrated development environment, or IDE (I recommend VS Code with the Julia extension).
• Use the IJulia.jl package. I've included this in the project environment (discussed below), so no further steps are needed.

2. Opening the notebook will depend on what you decided to do in the previous step.

• If you installed VS Code, you should be able to just open lab05.ipynb and everything should just work.
• If you're using a different IDE, Google how to make sure that it is set up to run a Julia notebook.
• If you want to use IJulia.jl, open a Julia prompt. You can do this by:

  • Using the Julia-1.8 or equivalent graphical program, type cd("BEE6940/labs") or whatever path points to your lab notebook folder;

  • Navigating to your BEE6940/labs/lab05 folder and typing julia to open the prompt.Then:

    import Pkg
    Pkg.activate(".")
    using IJulia
    notebook()

    and you can navigate to and open lab08.ipynb.

Learning Objectives

After completing this lab, students will be able to:

• use Turing.jl and Markov chain Monte Carlo to sample from the posterior of a model;
• assess convergence and quality of model fit using quantitative and visual diagnostics.

Dependencies

This notebook uses the following packages:

• DataFrames.jl
• DataFramesMeta.jl
• CSV.jl
• Plots.jl
• StatsBase.jl
• Optim.jl
• Distributions.jl
• StatsPlots.jl
• Turing.jl