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Thursday, August 20, 2015

Kalman Filters for Data Uncertainty

About data and uncertainty.    Though all  data is uncertain.    Easily implemented today.  This piece starts simple, but gets very technical.   The introduction though, is good.   We tested this once in the enterprise, but ended up not using it, for process reasons.    Our data was not real time or memory constrained.   Well worth knowing about.  But in practice in our industry, rarely used.

(Source)  " ... You can use a Kalman filter in any place where you have uncertain information about some dynamic system, and you can make an educated guess about what the system is going to do next. Even if messy reality comes along and interferes with the clean motion you guessed about, the Kalman filter will often do a very good job of figuring out what actually happened. And it can take advantage of correlations between crazy phenomena that you maybe wouldn’t have thought to exploit!

Kalman filters are ideal for systems which are continuously changing. They have the advantage that they are light on memory (they don’t need to keep any history other than the previous state), and they are very fast, making them well suited for real time problems and embedded systems. .. "

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