Join the official Python Developers Survey 2018 and win valuable prizes:

Functional ANOVA using Gaussian Process priors.

## Project description

# Gaussian Process Functional ANOVA

Implementation of a functional ANOVA (FANOVA) model, based partly on the model in

[Bayesian functional ANOVA modeling using Gaussian process prior distributions](http://projecteuclid.org/euclid.ba/1340369795). To implement a FANOVA model, an underlying general framework is defined for modeling functional observations:

\$\$ Y(t) = X beta(t),\$\$

where \$\$ Y(t) = [y_1(t),dots,y_m(t)]^T, \$\$ \$\$beta(t) = [beta_1(t),dots,beta_f(t)]^T,\$\$ \$\$ X: m times f\$\$ for a given time \$t\$. The design matrix \$X\$ defines the relation between the functions \$beta\$ and observations \$y\$. In general, the rank of \$X\$ should match the number of functions \$f\$. The FANOVA model can then be described by a specific form of \$X\$ such that

\$\$ y_{i,j}(t) = mu(t) + alpha_i(t) + beta_j(t) + alphabeta_{i,j}(t). \$\$