Monte Carlo
-----------
*Sequential importance sampling* is an instantiation of *importance sampling*, which approximates integrals by sampling.
The instantiation merely specifies that sampling takes place per dimension.
The technique becomes useful with :ref:`re-sampling `\ , which between dimensions redistributes the probability mass to the most likely regions of space.
.. autofunction:: probability.sequential_importance_resample.sequentialImportanceResample(targetFactors, proposers, number, weighters=…, combiners=…, combiners=…, emptySample=…)
The following are small helper functions.
.. autofunction:: probability.sequential_importance_resample.densityWeighter
.. autofunction:: probability.sequential_importance_resample.unnormalisedDensityWeighter
.. autofunction:: probability.sequential_importance_resample.massWeighter
.. autofunction:: probability.sequential_importance_resample.appendToVector
.. autofunction:: probability.sequential_importance_resample.appendToTuple
.. _resampling:
Re-sampling
^^^^^^^^^^^
The element that makes sequential importance re-sampling useful is re-sampling.
It approximates a categorical distribution by a distribution with integer weights.
It can be seen as a way of re-focusing the probability mass on high-probability areas of the space.
.. autofunction:: probability.categorical.resample(source, number, strategy=systematicResampling)
.. autofunction:: probability.categorical.multinomialResampling
.. autofunction:: probability.categorical.residualResampling
.. autofunction:: probability.categorical.systematicResampling