The Living Thing / Notebooks :

Particle filters

a.k.a. “Sequential Monte Carlo” and a profusion of other simultaneous-discovery names.

A randomised generalisation of state filter models such as the Kalman Filter.

Easy to explain with an example:

A scalable particle filter in scala

EDIT: Apparently SMC is more general - it does not necessarily assume that the additional axes are assimilated in time, but can index any arbitrary dimension of your data, as long as you are approximating the right likelihood. Regardless…


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