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.
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.