pypomp documentation

pypomp is a Python package for modeling and inference using partially observed Markov process (POMP) models, also called state-space models (SSM) or hidden Markov models (HMM). Key features include:

• Estimation, filtering, and inference for nonlinear, non-Gaussian POMP models via the particle filter

• GPU support and just-in-time compilation via JAX, enabling significant speedups

• New algorithms for model-fitting with gradient descent using improved gradient estimates

Getting started

• The tutorials provide introductory explanations of POMP models and methods in pypomp.

• The quantitative tests provide sample code used for benchmarking package performance.

• The pypomp organization GitHub site hosts source code and related projects.

The main classes in pypomp are:

• pypomp.Pomp - Core POMP model class

• pypomp.PanelPomp - Panel POMP models for multiple units

Contents

• Model Structure Components
  • State Initialization (rinit)
  • State Transition (rproc)
  • Measurement Density (dmeas)
  • Measurement Simulator (rmeas)
• Best Practices and Performance Guidelines
  • Essential Guidelines
• API Reference
  • Core Classes
  • Model Components
  • Utilities
  • Example Models

Indices

• Index

• Module Index

• Search Page