estimator: State-space estimation algorithms such as Kalman Filters

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The goal of this library is to simplify implementation and use of state-space estimation algorithms, such as Kalman Filters. The interface for constructing models is isolated as much as possible from the specifics of a given algorithm, so swapping out a Kalman Filter for a Bayesian Particle Filter should involve a minimum of effort.

This implementation is designed to support symbolic types, such as from sbv or ivory. As a result you can generate code in another language, such as C, from a model written using this package; or run static analyses on your model.

Also included is a sophisticated sensor fusion example in Numeric.Estimator.Model.SensorFusion, which may be useful in its own right.


Versions1.0.0, 1.0.0,,,,
Change logNone available
Dependenciesad (>=4.2), base (>=4.6 && <5), distributive (>=0.4), lens (>=4.6), linear (>=1.15), reflection (>=1.5) [details]
Copyright2014 Galois, Inc.
AuthorJamey Sharp
CategoryMath, Numerical, Statistics
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Source repositorythis: git clone 1.0.0)
UploadedFri Dec 5 23:55:33 UTC 2014 by JameySharp




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