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]||1.0.0, 18.104.22.168, 22.214.171.124, 126.96.36.199, 188.8.131.52|
|Dependencies||ad (>=4.2), base (>=4.6 && <5), distributive (>=0.4), lens (>=4.6), linear (>=1.16), reflection (>=1.5) [details]|
|Copyright||2014-2016 Galois, Inc.|
|Category||Math, Numerical, Statistics|
|Source repo||this: git clone https://github.com/GaloisInc/estimator(tag 184.108.40.206)|
|Uploaded||by AdamFoltzer at 2016-07-19T19:29:48Z|
|Reverse Dependencies||1 direct, 0 indirect [details]|
|Downloads||3824 total (3 in the last 30 days)|
|Rating||(no votes yet) [estimated by Bayesian average]|
|Status||Docs available [build log]
Last success reported on 2016-07-19 [all 1 reports]