estimator: State-space estimation algorithms such as Kalman Filters

[ bsd3, library, math, numerical, statistics ] [ Propose Tags ]

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.


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Versions [RSS] 1.0.0,,,,
Dependencies ad (>=4.2), base (>=4.6 && <5), distributive (>=0.4), lens (>=4.6), linear (>=1.16), reflection (>=1.5) [details]
License BSD-3-Clause
Copyright 2014-2016 Galois, Inc.
Author Jamey Sharp
Category Math, Numerical, Statistics
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Source repo this: git clone
Uploaded by AdamFoltzer at 2016-07-19T19:29:48Z
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Reverse Dependencies 1 direct, 0 indirect [details]
Downloads 3824 total (3 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2016-07-19 [all 1 reports]