.. _method-bayes_calibration-gpmsa-logit_transform:

"""""""""""""""
logit_transform
"""""""""""""""


Utilize the logit transformation to reduce sample rejection for bounded domains


.. toctree::
   :hidden:
   :maxdepth: 1



**Specification**

- *Alias:* None

- *Arguments:* None


**Description**


The logit transformation performs an internal variable transformation from bounded domains to unbounded domains in order to reduce sample rejection due to an out-of-bounds condition.

*Default Behavior*

This option is experimental at present, and is therefore defaulted off.

*Usage Tips*

This option can be helpful when regions of high posterior density exist
in the corners of a multi-dimensional bounded domain.  In these cases,
it may be difficult to generate feasible samples from the proposal density,
such that transformation to unbounded domains may greatly reduce sample
rejection rates.



**Examples**



.. code-block::

    method,
            bayes_calibration queso
              samples = 2000 seed = 348
              dram
              logit_transform




