Ledger's Bayesian workflow scripts

This repository contains the scripts used to reproduce the analyses in "A Bayesian workflow for securitizing casualty insurance risk" by Haines, Goold, & Shoun (2024).

The Bayesian models themselves are implemented in Stan scripts located in the bayesian-workflow-paper-2024/stan/ directory.

Install requirements

All analyses were run with Python 3.11. Once Python 3.11 is installed locally, we recommend the following steps:

1. navigate to your local bayesian-workflow-paper-2024/ directory
2. initialize a virtual environment: python3.11 venv env
3. activate the environment: source env/bin/activate
4. install requirements: pip install -r requirements/requirements.txt

Reproduce analyses

Analyses can then be reproduced by running the following scripts in order:

1. download and pre-process the data: python pull.py
2. run simulation-based calibration: python sbc.py
3. run backtests along with prior and posterior predictive checks: python backtest.py

Once analyses are reproduced, figures are located in the bayesian-workflow-paper-2024/figures/ directory.