Hello everyone,
I noticed that scikit-learn (and Python in general) seems to be missing a
decent module for State Space Models. State Space Models are a type of
generative model wherein one attempts to estimate the hidden state of a
system given a sequence of noisy observations. Observations at a time
step 't' are assumed conditionally independent of all other variables given
the hidden state at time 't', and knowing the hidden state at time 't'
makes all variables before 't' conditionally independent of all other
variables after time 't'.
The most well-known examples include systems where all states and
observations are discrete (a la the already-implemented HMM) or distributed
according to a Gaussian distribution (see Kalman Filter, Kalman Smoothing).
When the models don't fall into these two cases, Monte Carlo methods such
as the Particle Filter are often applied.
I am proposing to extend the HMM module with an implementation of the
Kalman Filter, Unscented Kalman Filter, and possibly the Particle Filter if
the community believes it appropriate.
This will be my first time committing more than a fix to an Open Source
project, so I would highly appreciate any comments on what the typical
process for proposing additions is, how to merge them into a larger project
like scikit-learn, and any other advice you may have.
Daniel Duckworth
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