cokm.param

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

This function compute estimates for regression and variance
 parameters given the correlation parameters are known. It is used to show
all model parameters in one place.

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

cokm.param function

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

cokm.param: Get model parameters in the autoregressive cokriging model

Description

Usage

Arguments

Value

See Also