mvcokm.predict

a <code><a rd-options="" href="/link/mvcokm?package=ARCokrig&version=0.1.2" data-mini-rdoc="ARCokrig::mvcokm">mvcokm</a></code> object construted via the function <code><a rd-options="" href="/link/mvcokm?package=ARCokrig&version=0.1.2" data-mini-rdoc="ARCokrig::mvcokm">mvcokm</a></code> in 
this package

a matrix including new inputs for making prediction

input.new

This function makes prediction in
 the parallel partial cokriging model. If a nested design is used, the predictive mean and predictive variance are
 computed exactly; otherwise, Monte Carlo simulation from the predictive distribution is used to approximate
the predictive mean and predictive variance.

For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) <DOI:10.1137/19M1289893>. The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2019) <arXiv:1909.01836>.

Pulong Ma

ARCokrig

Autoregressive Cokriging Models for Multifidelity Codes

mvcokm.predict function

a <code><a rd-options='' href='mvcokm'>mvcokm</a></code> object construted via the function <code><a rd-options='' href='mvcokm'>mvcokm</a></code> in 
this package

mvcokm.predict: Prediction at new inputs in autoregressive cokriging models for multivarite output

Description

Usage

Arguments

See Also