Core concepts#
An SMC run passes a particle population, a context, and a source of randomness through a sequence of steps. These guides explain how those objects fit together and how the choice of array backend affects your target functions.
Array backends explains backend selection, SciPy Array API setup, and writing target functions that use the same namespace as the particles.
Particle state and results introduces particle locations, weights, and metadata, how to return updated states, and how the result records iteration history.
Step context describes shared inputs, evolving run metadata, observations, and stop requests used to coordinate steps and callbacks.
Random number generation covers seeds, generator ownership, and explicit random keys for reproducible eager and functional execution.
Read these guides before building a custom pipeline or implementing new steps. To configure a sampler and interpret its output, continue with Running inference; to implement your own components, see Extending Sequince.