Blurb::
Perform deterministic optimization for MAP before Bayesian calibration
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.
Topics::

Examples::

.. code-block::

    method
      bayes_calibration queso
        samples = 2000 seed = 348
        delayed_rejection
        emulator
          pce sparse_grid_level = 2
          pre_solve nip # default for emulators


Theory::

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