rcalibration_MS-class {RobustCalibration} | R Documentation |
Robust Calibration for multiple sources class
Description
S4 class for multiple sources Robust rcalibration with or without the specification of the discrepancy model.
Objects from the Class
Objects of this class are created and initialized with the function rcalibration_MS
that computes the prediction after calibrating the mathematical models from multiple sources.
Slots
num_sources
:Object of class
integer
. The number of sources.p_x
:Object of class
vector
. Each element is the dimension of the observed inputs in each source.p_theta
:Object of class
integer
. The number of calibration parameters.num_obs
:Object of class
vector
.Each element is the number of experimental observations of each source.index_theta
:Object of class
list
. The each element is avector
of the index of calibration parameters (theta) contained in each source.input
:Object of class
list
. Each element is amatrix
of the design of experiments in each source with dimensionn_i x p_{x,i}
, for i=1,...,num_sources.output
:Object of class
list
. Each element is avector
of the experimental observations in each source with dimension n_i x 1, for i=1,...,num_sources.X
:Object of class
list
. Each element is amatrix
of the mean/trend discrepancy basis function in each source with dimension n_i x q_i, for i=1,...,num_sources.have_trend
:Object of class
vector
. Each element is abool
to specify whether the mean/trend discrepancy is zero in each source. "TRUE" means it has zero mean discrepancy and "FALSE"" means the mean discrepancy is not zero.q
:Object of class
vector
. Each element isinteger
of the number of basis functions of the mean/trend discrepancy in each source.R0
:Object of class
list
. Each element is a list of matrices where the j-th matrix is an absolute difference matrix of the j-th input vector in each source.kernel_type
:Object of class
vector
. Each element is acharacter
to specify the type of kernel to use in each source.alpha
:Object of class
list
. Each element is avector
of parameters for the roughness parameters in the kernel in each source.theta_range
:A
matrix
for the range of the calibration parameters.lambda_z
:Object of class
vector
. Each element is anumeric
value about how close the math model to the reality in squared distance when the S-GaSP model is used for modeling the discrepancy in each source.S
:Object of class
integer
about how many posterior samples to run.S_0
:Object of class
integer
about the number of burn-in samples.prior_par
:Object of class
list
. Each element is avector
about prior parameters.output_weights
:Object of class
list
. Each element is avector
about the weights of the experimental data.sd_proposal_theta
:Object of class
vector
about the standard deviation of the proposal distribution for the calibration parameters.sd_proposal_cov_par
:Object of class
list
. Each element is avector
about the standard deviation of the proposal distribution for the calibration parameters in each source.discrepancy_type
:Object of class
vector
. Each element is acharacter
about the type of the discrepancy in each source. If it is 'no-discrepancy', it means no discrepancy function. If it is 'GaSP', it means the GaSP model for the discrepancy function. If it is 'S-GaSP', it means the S-GaSP model for the discrepancy function.simul_type
:Object of class
vector
. Each element is aninteger
about the math model/simulator. If the simul_type is 0, it means we use RobustGaSP R package to build an emulator for emulation. If the simul_type is 1, it means the function of the math model is given by the user. When simul_type is 2 or 3, the mathematical model is the geophyiscal model for Kilauea Volcano. If the simul_type is 2, it means it is for the ascending mode InSAR data; if the simul_type is 3, it means it is for the descending mode InSAR data.emulator_rgasp
:Object of class
list
. Each element is an S4 class ofrgasp
from the RobustGaSP package in each source.emulator_ppgasp
:Object of class
list
. Each element is an S4 class ofppgasp
from the RobustGaSP package in each source.post_theta
:Object of class
matrix
for the posterior samples of the calibration parameters after burn-in.post_individual_par
:Object of class
list
. Each element is amatrix
for the posterior samples after burn-in in each source.post_value
:Object of class
vector
for the posterior values after burn-in.accept_S_theta
:Object of class
numerical
for the number of proposed samples of the calibration parameters are accepted in MCMC.accept_S_beta
:Object of class
vector
for the number of proposed samples of the range and nugget parameters in each source are accepted in MCMC.count_boundary
:Object of class
vector
for the number of proposed samples of the calibation parameters are outside the range and they are rejected directly.have_measurement_bias_recorded
:Object of class
bool
for whether measurement bias will be recorded or not.measurement_bias
:Object of class
bool
for whether measurement bias exists or not.post_delta
:Object of class
matrix
of samples of model discrepancy.post_measurement_bias
:Object of class
list
of samples of measurement_bias if measurement bias is chosen to be recorded.thinning
:Object of class
integer
for the ratio between the number of posterior samples and the number of samples to be recorded.emulator_type
:Object of class
vector
for the type of emulator for each source of data. 'rgasp' means scalar-valued emulator and 'ppgasp' means vectorized emulator.loc_index_emulator
:Object of class
list
for location index to output in ghe ppgasp emulator for computer models with vectorized output.
Methods
- predict_MS
See
predict_MS
.
Author(s)
Mengyang Gu [aut, cre]
Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>
References
A. O'Hagan and M. C. Kennedy (2001), Bayesian calibration of computer models, Journal of the Royal Statistical Society: Series B (Statistical Methodology, 63, 425-464.
M. Gu (2016), Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output, Ph.D. thesis., Duke University.
M. Gu and L. Wang (2017) Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction. arXiv preprint arXiv:1707.08215.
M. Gu (2018) Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection . arXiv preprint arXiv:1804.09329.
See Also
rcalibration_MS
for more details about how to create a rcalibration_MS
object.