tutorials

pypomp Tutorials & Courses

Welcome to the tutorials repository for pypomp, a Python/JAX library for modeling and simulation-based inference with partially observed Markov process (POMP) models.

This repository contains tutorials designed to help you build models and perform inference using sequential Monte Carlo techniques, GPU acceleration, and automatic differentiation with pypomp.

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🚀 Quick Links

• 📖 Read the Documentation — Core library API reference and user guide.
• 💻 Core Repository — Source code and issue tracker for the pypomp package.
• 🎓 Tutorials Repository — Quarto notebooks and materials for learning pypomp.

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📚 Tutorial & Course Catalog

1. Introduction to Pypomp

A step-by-step introduction demonstrating how to construct a POMP model (specifically, a linear Gaussian state-space model) from scratch. It guides you through state simulation, calculating log-likelihoods, and running iterated filtering (IF2) for parameter estimation.

2. Inference for Cholera Dynamics in Dhaka using DMOP

A tutorial demonstrating how to construct a POMP model for cholera dynamics in Dhaka from scratch and how to perform parameter estimation using the Differentiated Measurement Off-Parameter (DMOP) filter with the Adam optimizer.

3. Fitting a Large Panel Model for UK Measles

A tutorial demonstrating how to construct and fit a large spatial panel model (with weekly case reports from 1,422 cities/towns in England and Wales) using Marginalized Panel Iterated Filtering (MPIF).

4. Simulation-based Inference for Epidemiological Dynamics (SBIED)

A short course designed for readers interested in POMP modeling, using epidemiological dynamics as an example. The course transfers concepts from the R-pomp short course to pypomp.

Course Lessons:

• Lesson 0: Preparing your laptop environment
• Lesson 1: Introduction: What is SBIED? POMPs and Pypomp
• Lesson 2: Simulation of stochastic dynamic models
• Lesson 3: Likelihood for POMPs: theory and practice
• Lesson 4: Iterated filtering: theory and practice
• Lesson 5: Case study: Measles (recurrent epidemics, covariates)
• Lesson 6: Case study: Ebola (model diagnostics and forecasting)

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💬 Community and Support

• Docs: Refer to the core Read the Docs page.
• Bugs & Features: Please open an issue or submit a pull request on our main GitHub repository.
• Contact: Pypomp is under active development. Please get in touch if you are reading this and you have feedback! You can email the core team at pypomp-org@umich.edu or reach out via GitHub.

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