Skip to main content

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

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for gpfanova, version 0.1.14
Filename, size File type Python version Upload date Hashes
Filename, size gpfanova-0.1.14.tar.gz (106.9 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page