For anyone wanting to dig deeper, GPML is the bible on Gaussian Processes: http://www.gaussianprocess.org/gpml/chapters/ http://www.gaussianprocess.org/gpml/cha

For anyone wanting to dig deeper, GPML is the bible on Gaussian Processes: http://www.gaussianprocess.org/gpml/chapters/ http://www.gaussianprocess.org/gpml/chapters/ GP's are awesome. Usually, if you've got a few points, your first approach to "fitting a curve through them" would be to choose some parametric form and hope that you're not overfitting with e.g. too high a degree of polynomial. But what if you've got something piecewise, with unpredictable pieces? And what if you don't need a writable parametric form for your curve, but all you need is to answer the question "given my data, what's the probability distribution over the y value at x=5, 6, 7, 8, weighted over all likely curves that might fit my data based on how well they fit?" Then a GP fit on your data will work wonders for you, as essentially an oracle for those kinds of queries. (And you can then just use the means of those distributions sampled at small intervals, if all you want is to throw a single curve on the screen.) Depending on your choices of priors and kernels, you can have it do magical things.