pre_solve
Perform deterministic optimization for MAP before Bayesian calibration
Specification
Alias: None
Arguments: None
Default: nip pre-solve for emulators
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
---|---|---|---|
Required (Choose One) |
Pre-solve Optimizer |
Use a sequential quadratic programming method for solving an optimization sub-problem |
|
Use a nonlinear interior point method for solving an optimization sub-problem |
|||
Deactivates MAP pre-solve prior to initiating the MCMC process. |
Description
When specified, Dakota will perform a deterministic derivative-based optimization to maximize the log posterior (minimize the negative log posterior = misfit - log_prior + constant normalization factors). The Markov chain in Bayesian calibration will subsequently be started at the best point found in the optimization (the MAP point), which can eliminate the need for “burn in” of the chain in which some initial portion of the chain is discarded.
Examples
method
bayes_calibration queso
samples = 2000 seed = 348
delayed_rejection
emulator
pce sparse_grid_level = 2
pre_solve nip # default for emulators