Bayesian Non-Parametric Dependent Models for Time-Indexed Functional Data
Plot estimated functions for experimental units faceted by cluster ver...
Side-by-side plot panels that compare latent function values to data f...
Generate a finite population and take an informative single or two-sta...
Run a Bayesian functional data model under an instrinsic GMRF prior wh...
Bayesian instrinsic Gaussian Markov Random Field model for dependent t...
Run a Bayesian functional data model under an instrinsic GMRF prior wh...
Run a Bayesian functional data model under a GP prior with a fixed clu...
Run a Bayesian functional data model under a GP prior whose parameters...
Bayesian non-parametric dependent Gaussian process model for time-inde...
Run a Bayesian functional data model under a GP prior whose parameters...
Run a Bayesian functional data model under a GP prior whose parameters...
Run a Bayesian functional data model under a GP prior whose parameters...
Bayesian Non-Parametric Models for Estimating a Set of Denoised, Laten...
Plot credible intervals for parameters to compare ignoring with weight...
Compute normalized mean squared prediction error based on accuracy to ...
Plot estimated functions, faceted by cluster numbers, for a known clus...
Use the model-estimated iGMRF precision parameters from gmrfdpgrow() t...
Use the model-estimated GP covariance parameters from gpdpgrow() to pr...
Use the model-estimated covariance parameters from gpdpgrow() or gmrdp...
Plot estimated functions both at estimated and predicted time points w...
Produce samples of MCMC output
Produce samples of MCMC output
Estimates a collection of time-indexed functions under either of Gaussian process (GP) or intrinsic Gaussian Markov random field (iGMRF) prior formulations where a Dirichlet process mixture allows sub-groupings of the functions to share the same covariance or precision parameters. The GP and iGMRF formulations both support any number of additive covariance or precision terms, respectively, expressing either or both of multiple trend and seasonality.