Authors: Brian Beckman
Kalman filtering is commonplace in engineering, but less familiar to software developers. It is the central tool for estimating states of a model, one observation at a time. It runs fast in constant memory. It is the mainstay of tracking and navigation, but it is equally applicable to econometrics, recommendations, control: any application where we update models over time. By writing a Kalman filter as a functional fold, we can test code in friendly environments and then deploy identical code with confidence in unfriendly environments. In friendly environments, data are deterministic, static, and present in memory. In unfriendly, real-world environments, data are unpredictable, dynamic, and arrive asynchronously. The flexibility to deploy exactly the code that was tested is especially important for numerical code like filters. Detecting, diagnosing and correcting numerical issues without repeatable data sequences is impractical. Once code is hardened, it can be critical to deploy exactly the same code, to the binary level, in production, because of numerical brittleness. Functional form makes it easy to test and deploy exactly the same code because it minimizes the coupling between code and environment.
Comments: 19 Pages.
[v1] 2016-06-29 14:21:33
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