Assignment 1: Sampler Synthesis

⏰ Due: Wednesday, February 18 | 15% of final grade

Design, implement, and critically analyze a novel MCMC sampling method.

📄 Assignment description (PDF) | 🤖 AI coding assistants guide

Overview

In this assignment, you'll work with AI coding assistants to create a new MCMC sampler and rigorously evaluate it against standard baselines (Random Walk MH and HMC) on two challenging benchmark distributions.

Structure

├── assignment-01.pdf             # Assignment description
├── assignment-01-starter.ipynb   # Starter code with baselines and benchmarks
├── requirements.txt              # Python dependencies
└── report/
    ├── report.tex                # Your report (NeurIPS format)
    └── neurips_2025.sty          # LaTeX style file

Getting Started

1. Accept the assignment

Click the GitHub Classroom link shared by the instructor. This creates your own private copy of this repository.

2. Clone your repository

git clone https://github.com/bu-ds595/assignment01-YOUR_USERNAME.git
cd assignment01-YOUR_USERNAME

3. Install dependencies

pip install -r requirements.txt

4. Open the notebook

VS Code: Open the folder, then open assignment-01-starter.ipynb

JupyterLab: Run jupyter lab and open the notebook

Google Colab: Upload the notebook and run:

!pip install jax jaxlib blackjax arviz

Deliverables

1. Code/Notebook — Your sampler implementation with experiments and visualizations
2. Written Report — Maximum 3 pages in NeurIPS format (use report/report.tex)

Submitting

git add .
git commit -m "Complete assignment 1"
git push

You can push multiple times. Only the final version at the deadline will be graded.

Resources

• Assignment description (PDF)
• AI coding assistants guide
• Course notes: MCMC & Variational Inference
• BlackJAX documentation
• ArviZ documentation