Domino Data Science Blog

Chris Fonnesbeck

Chris Fonnesbeck is a professor of biostatistics at Vanderbilt University and, as of recent, Principal Quantitative Analyst at the Philadelphia Phillies.

Data Science

Fitting Gaussian Process Models in Python

A common applied statistics task involves building regression models to characterize non-linear relationships between variables. It is possible to fit such models by assuming a particular non-linear functional form, such as a sinusoidal, exponential, or polynomial function, to describe one variable's response to the variation in another. Unless this relationship is obvious from the outset, however, it involves possibly extensive model selection procedures to ensure the most appropriate model is retained. Alternatively, a non-parametric approach can be adopted by defining a set of knots across the variable space and use a spline or kernel regression to describe arbitrary non-linear relationships. However, knot layout procedures are somewhat ad hoc and can also involve variable selection. A third alternative is to adopt a Bayesian non-parametric strategy, and directly model the unknown underlying function. For this, we can employ Gaussian process models.

By Chris Fonnesbeck27 min read

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