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Probabilistic inference

Before concluding we provide a brief sketch of probabilistic inference in Birch.

Note

This section will be expanded in future.

Inference methods include those of the ParticleFilter class hierarchy, for sequentially filtering a model, and of the ParticleSampler class hierarchy, which build on these to draw samples from the posterior distribution.

Specialized particle filters include the AliveParticleFilter for situations where weights may be zero, and the MoveParticleFilter for resample-move using gradient-based kernels. Additionally, automatic marginalization and automatic conjugacy provide for Rao–Blackwellization and adaptation in the sense of the auxiliary particle filter.

As a model runs it emits an event every time a simulate (<~), observe (~>) or assume (~) operator executes. The inference method registers an appropriate event handler (from the Handler hierarchy) to handle these. The events provide insight into the model, and a means to influence its execution. The inference method may, for example, implement:

  • Importance sampling by using a combination of simulation and observation to compute importance weights.

  • Particle filtering or Sequential Monte Carlo by extending importance sampling with resampling between epochs.

  • Particle Gibbs.