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A comment on 20 lines of code that beat A/B testing every time. Parent comment

Re:20 lines of code that beat A/B testing every time by JeanPierre on Tuesday May 29, 2012 @07:36PM (#4040556)
How does a decay factor not "trust your 'new' data more than really 'old' data"?
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Re:20 lines of code that beat A/B testing every time by IgorPartola on Tuesday May 29, 2012 @08:30PM (#4040741)
The Kalman filter is much more sophisticated. Typical re-estimation will be:

  x1 = x0 + alpha * (z - x0)
where alpha is static. The Kalman filter will make it dynamic, taking into account how you obtained the measurements, how old the last re-estimation was, how noisy the process is, etc. Want to do multi-variate analysis? Make alpha a matrix transform.
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