Data Representation: Bayesian Approach That's Sparse
Main simulation function
Project a set of curves onto a histogram basis
Fit a Bayesian Latent Factor to a data set using STAN
Perform a PCA using Deville's method
PCA data projected onto a histogram basis
Calculate the unnormalized posterior density of the model
Format scores output for visualization
Plot the estimates for the latent factors
Build and decompose a low-rank matrix W
Perform a weighted PCA using Deville's method on a data matrix X that ...
Calculate the log likelihood of the model
Convert a STAN objet to MCMC list
Perform Coinertia Analysis on the PCA of the Weighted PCA and Deville'...
Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.