The QLIM procedure implements the Hamiltonian Monte Carlo No-U-Turn Sampler (NUTS) with transformation of the bounded parameters. For more information about NUTS and more in general about Hamiltonian Monte Carlo, see the section Hamiltonian Monte Carlo Sampler (SAS/STAT User's Guide).
The Bayesian analysis is primarily interested in the properties of the posterior distribution,
where is the parameter vector associated with the model and
represents the data. The properties of the model and the properties of the prior distribution can impose restrictions on the domain of
. These restrictions can reduce the efficiency of the common sampling methods. One way to improve the efficiency is to perform a parameter transformation, which maps the bounded parameters
to the unbounded parameter
. In a simplified scenario, four cases can be identified:
The corresponding inverse transformations are
with partial derivatives
Given the independent nature of the transformation, the corresponding Jacobian is a diagonal matrix
which in turn implies that
It is usually convenient to work on the logarithmic scale,
where