Kalman filters and functional programming
A few weeks ago I started a series of posts on various things you could do with a functional fold. In the first post I mentioned that the idea came from a paper by Brian Beckman on Kalman filters and folds: This post was inspired by a paper by Brian Beckman (in progress) that shows how a Kalman filter can be implemented as a fold. From a Bayesian perspective, the thing that makes the Kalman filter
Literate programming: presenting code in human order
Presentation order People best understand computer programs in a different order than compilers do. This is a key idea of literate programming, and one that distinguishes literate programs from heavily commented programs. Traditional source code, no matter how heavily commented, is presented in the order dictated by the compiler. The computer is the primary audience. Literate programming is more h
Accurately computing sample variance online
The most direct way of computing sample variance or standard deviation can have severe numerical problems. Mathematically, sample variance can be computed as follows. The most obvious way to compute variance then would be to have two sums: one to accumulate the sum of the x‘s and another to accumulate the sums of the squares of the x‘s. If the x‘s are large and the differences between them small,
100x better approach to software?
Alan Kay speculates in this talk that 99% or even 99.9% of the effort that goes into creating a large software system is not productive. Even if the ratio of overhead and redundancy to productive code is not as high as 99 to 1, it must be pretty high. Note that we’re not talking here about individual programmer productivity. Discussions of 10x or 100x programmers usually assume that these are folk
Diagram of probability distribution relationships
Diagram of distribution relationships Probability distributions have a surprising number inter-connections. A dashed line in the chart below indicates an approximate (limit) relationship between two distribution families. A solid line indicates an exact relationship: special case, sum, or transformation. Click on a distribution for the parameterization of that distribution. Click on an arrow for d
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Error in the normal approximation to the binomial distribution