queso
Markov Chain Monte Carlo algorithms from the QUESO package
Topics
bayesian_calibration, package_queso
Specification
Alias: None
Arguments: None
Child Keywords:
Required/Optional 
Description of Group 
Dakota Keyword 
Dakota Keyword Description 

Required 
Number of Markov Chain Monte Carlo posterior samples 

Optional 
Seed of the random number generator 

Optional 
Selection of a random number generator 

Optional 
Use an emulator or surrogate model to evaluate the likelihood function 

Optional 
Perform Bayesian inference in standardized probability space 

Optional 
Utilize the logit transformation to reduce sample rejection for bounded domains 

Optional 
Export the MCMC chain to the specified filename 

Optional (Choose One) 
MCMC Algorithm 
Use the DRAM MCMC algorithm 

Use the Delayed Rejection MCMC algorithm 

Use the Adaptive Metropolis MCMC algorithm 

Use the MetropolisHastings MCMC algorithm 

Use the multilevel MCMC algorithm. 

Optional 
Perform deterministic optimization for MAP before Bayesian calibration 

Optional 
Defines the technique used to generate the MCMC proposal covariance. 

Optional 
File containing advanced QUESO options 
Description
The queso
method supports the following MCMC algorithms: DRAM
(Delayed Rejection Adaptive Metropolis), delayed rejection (DR) only,
adaptive metropolis (AM) only, pure Metropolis Hasting (MH)s, and
multilevel (ML).
When calibrating fastrunning simulation models, use of an emulator is
not typically warranted. For slower models, using an emulator model
in the MCMC sampling will greatly improve the speed, since the Monte
Carlo Markov Chain will generate thousands of samples on the emulator
instead of the real simulation code. An emulator may be specified
with the keyword emulator
, followed by a gaussian_process
emulator, a pce
emulator (polynomial chaos expansion), or a sc
emulator (stochastic collocation). For the gaussian_process
emulator, the user must specify whether to use the surfpack
or
dakota
version of the Gaussian process. The user can define the
number of samples build_samples
from which the emulator should be
built. It is also possible to build the Gaussian process from points
read in from the import_points_file
and to export
approximationbased sample evaluations using
export_points_file
. For pce
or sc
, the user can define a
sparse_grid_level
.
There are a variety of ways the user can specify the proposal covariance matrix which
is very important in governing the samples generated in the chain.
The proposal covariance specifies the covariance structure of a multivariate normal distribution.
The user can specify proposal_covariance
, followed by derivatives
, prior
,
values
, or filename
. The derivative specification involves
forming the Hessian of the misfit function (the negative
log likelihood). When derivative information is available inexpensively (e.g. from an emulator),
the derivedbased proposal covariance forms a more
accurate proposal distribution, resulting in lower rejection rates and
faster chain mixing. The prior setting involves constructing the proposal from the variance of the prior distributions of the parameters being calibrated. When specifying the proposal covariance with values or from a file, the user
can choose to specify only the diagonals of the covariance matrix with diagonal
or to specify the full covariance matrix with matrix
.
There are two other controls for QUESO.
The pre_solve
option enables the user to start the chain at an optimal point, the
Maximum A Posteriori (MAP) point. This is the point in parameter space that maximizes
the log posterior, (defined as the loglikelihood minus the log_prior). A deterministic optimization method is used to obtain the MAP point, and the MCMC chain is then
started at the best point found in the optimization.
The second factor is a logit_transform
,
which performs an internal variable transformation from bounded domains to unbounded domains
in order to reduce sample rejection due to an outofbounds condition.
Note that as of Dakota 6.2, the field data capability may be used with QUESO. That is, the user can specify field simulation data and field experiment data, and Dakota will interpolate and provide the proper residuals to the Bayesian calibration.