Bayesian inference

Description

This project performs Bayesian inference using NUTS sampling (a form of Markov Chain Monte Carlo sampling) to probe the position and source intensity of a lighthouse, based on lighthouse flash data in lighthouse_flash_data.txt. The analysis report can be found in report/.

Project structure

├── report/  # contains report
├── docs/    # Contains auto-documentation for the project (generated using the `Doxyfile`).
├── plots/   # directory for storing plots used in the report
├── src/     # source code (used in main.py)
├── main.py  # main script for the project
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├── lighthouse_flash_data.txt  # dataset
├── lighthouse.pdf  # coursework assignment
├── .gitignore              # specifies untracked files to ignore
├── Dockerfile              # containerisation instructions
├── LICENSE                 # license for project
├── README.md               # this file
└── environment.yml         # environment specifications

Usuage

To re-create the environment for the project you can either use conda or docker. Navigate to the root directory of the project and use one of the following:

# Option 1: re-create conda environment
$ conda env create -f environment.yml -n <env-name>

# Option 2: Generate docker image and run container
$ docker build -t <image_name> .
$ docker run -ti <image_name>

To re-produce all results/figures presented in the report, use:

$ python main.py --data ./lighthouse_flash_data.txt --output_dir ./plots

The plots will be saved to --output_dir and other results will be printed to the terminal.

Timing

The main script took ~2 minutes to run on my personal laptop with the following specifications:

• Chip: Apple M1 Pro
• Total Number of Cores: 8 (6 performance and 2 efficiency)
• Memory (RAM): 16 GB
• Operating System: macOS Sonoma v14.0