estimator: State-space estimation algorithms such as Kalman Filters
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.
|Versions [RSS] [faq]||1.0.0, 126.96.36.199, 188.8.131.52, 184.108.40.206, 220.127.116.11|
|Dependencies||ad (>=4.2), base (>=4.6 && <5), distributive (>=0.4), lens (>=4.6), linear (>=1.15), reflection (>=1.5) [details]|
|Copyright||2014 Galois, Inc.|
|Category||Math, Numerical, Statistics|
|Source repo||this: git clone https://github.com/GaloisInc/estimator(tag 18.104.22.168)|
|Uploaded||by JameySharp at 2014-12-10T23:55:31Z|
|Downloads||3595 total (11 in the last 30 days)|
|Rating||(no votes yet) [estimated by Bayesian average]|
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