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

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

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Filename, size & hash SHA256 hash help | File type | Python version | Upload date |
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gpfanova-0.1.14.tar.gz (106.9 kB) Copy SHA256 hash SHA256 | Source | None | Oct 2, 2016 |