MIX-SPEC:  Specify a mixture model, or display existing specification.

Mix-spec creates a log file containing the specifications for a
mixture model, along with the associated priors over hyperparameters.
When invoked with just a log file as an argument, it displays the
specifications for the mixture model stored in that log file.


    mix-spec log-file N-inputs N-targets [ N-components ]
             / concentration SD-prior mean-prior 


    mix-spec log-file

N-inputs and N-targets are the numbers of input variables and target
variables in the model.  The model tries to represent the joint
distribution of the targets, using a mixture.  At present, the number
of inputs (N-inputs) must be 0, but in future, input variables may be
allowed, in which case the model will define the joint conditional
distributions for the target values given observed values for the

N-components is the number of components in the mixture.  If it is
omitted, the number of components is countably infinite, giving the
effect of a Dirichlet process mixture.

The concentration argument specifies the concentration parameter for
the Dirichlet distribution for the component probabilities.  At
present, this must be fixed.  If the value starts with "x", the value
given is automatically scaled downward by the number of components.
The "x" must be present if the number of components is infinite (the
default), as this is the scaling needed for the finite model to reach
a sensible limit.

Each component of the mixture has associated with it a set of "offset"
parameters, one for each target value.  The prior distribution of
these offsets is defined by top-level means and standard deviations
for each target value.  The top-level means are variable hyperparameters, 
which are given independent Gaussian priors with mean zero and standard
deviation as specified by the mean-prior argument of mix-spec.  The
top-level standard deviations may be fixed or variable, as specified
by the SD-prior argument of mix-spec, using the general form of
specification described in prior.doc.  If the SD-prior argument starts 
with "x", it is automatically scaled according to the number of target
values, as described in prior.doc.

The specification of the model is completed using the model-spec
command (see model-spec.doc), which specifies whether the model is for
binary or real data.  For real data, it also specifies the prior for
the "noise" standard deviations, in a three-level hierarchical form,
which allows for a top-level standard deviation common to all target 
values and components, a standard deviation for each target value,
common to all components, and standard deviations for each target value 
for each component.

            Copyright (c) 1997 by Radford M. Neal