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We use another computational process, called the smoother, to smooth this raw SVM output. The smoother uses a version of a well-known mathematical tool called a Hidden Markov Model to correct, or “smooth,” two kinds of errors. Hidden Markov Models are used to analyze sequential data, like biological sequences or recorded speech. As an example, suppose we had three ancestry populations: X, Y, and Z. An example of output from the SVM might look like this:
chromosome 1, parent 1: X - X - X - Z - Z - Z - Y - Z
chromosome 1, parent 2: Z - Z - Z - X - X - X - X - X
The first kind of error the smoother corrects is an unusual assignment in the middle of a run of similar assignments. In the first line above, there's a run of Z's, interrupted by a single Y: Z — Z — Z — Y — Z. It's possible that the lone Y was a close call between Y and Z that went the wrong way. If that were the case, the smoother could correct it to Z — Z — Z — Z — Z.
