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EXAMPLES OF CIRCULARLY-COUPLED MARKOV CHAIN SAMPLING

Circular coupling is a new method for diagnosing covergence and
discarding "burn-in" iterations, described in my technical report on
"Circularly-coupled Markov chain sampling".  The examples here
demonstrate this software's facilities for simple circular coupling,
using "xxx-wrap" (see xxx-wrap.doc), and for circular coupling with
multiple starting points (possibly done in parallel), using "xxx-circ"
(see xxx-circ.doc).

Note that although these facilities are implemented in a way that is
designed for general use, not all aspects of the software have been
adapted to this scheme.  Also, the coupling techniques used are still
being improved.  Presently, circular coupling cannot be used at all
with mixture models, and it is probably not yet useful for most neural
network models.  It can be used for regression models using Gaussian
processes (if appropriate Markov chain operations are used), but not
for classification models, or other models requiring latent variables.

More substantial examples will be added in a future release of this
software, to go with a future revised version of the tech report.

The command files for these examples are in ex-circ.

Circularly-coupled sampling for a Cauchy model.

To start, we can look at a simple example of a Bayesian model for data
that is Cauchy distributed.  There is just one parameter, u, for this
model, which is the location of the Cauchy distribution (the scale is
fixed at one).  The prior for u will be Normal(0,20^2), and two data
points will be assumed to have been observed, with values of 18 and 25.

Here is a specification for the resulting posterior distribution:

> dist-spec clog "u~Normal(0,20^2)" "Log[1+(u-18)^2] + Log[1+(u-25)^2]"

Here, the two data values have been put into the likelihood expression
as constants.  There will therefore be no data file for this example.
The log file is written as "clog" above, but other names will be used
for the examples below.

One way of using circular coupling is to first simulate a chain in the
usual way, and then "wrap the chain around" in order to discard an
appropriate "burn-in" period, which is not from the stationary
distribution of the chain.  For this to work, however, we have to use
Markov chain operations that couple appropriately.  For one
dimensional problems, "random grid" Metropolis updates work well.  We
can specify such updates (with stepsize of 5) with the following
command, issued after the dist-spec command:

> dist-spec clog.forw "u~Normal(0,20^2)" "Log[1+(u-18)^2] + Log[1+(u-25)^2]"
> mc-spec clog.forw rgrid-met 5

The log file "clog.forw" will be the normal, "forward" version of the
chain.  We can run the chain for 500 iterations as follows:

> dist-mc clog.forw 500

You can now plot the progress of the chain over time, starting from
its default initial state of u=0, using the following command:

> dist-plt t u clog.forw | plot

Clearly, the initial portion of the chain is not representative of the
stationary distribution.  But it's not clear exactly how many
Markov chain sampling methods, we'd just have to make a somewhat

As an alternative, we can wrap the chain around - running it again
with log file clog.wrap, using the last state of clog.forw as the
start state, with the same random numbers as before.  This is done
with the following command:

> dist-wrap clog.forw clog.wrap

The dist-wrap procedure will recognize when (if ever) the new chain
coalesces with the old chain (ie, reaches the same state).  Once that
occurs, there is no need to perform any further computation - the
remaining iterations in clog.wrap can simply be copied from clog.forw.

For this example, coalescence will probably occur within a few dozen
iterations.  The wrapped around chain can be plotted as follows:

> dist-plt t u clog.wrap | plot

Provided certain assumptions about rapid coalescence are satisfied,
all the iterations in clog.wrap will come from approximately the right
distribution.

For other problems, however, it is possible that the wrapped-around
chain will not coalese with the original chain.  In this case, the
procedure must be repeated with a larger number of iterations.  Even
if coalescence does occur, however, it is possible that it takes too
long, on average - violating the assumptions needed for the answer to
be guaranteed to be (approximately) correct.

These assumptions can be tested by running chains from many starting
points, which this software draws from the prior distribution.  This
is done as follows:

> dist-spec clog.circ "u~Normal(0,20^2)" "Log[1+(u-18)^2] + Log[1+(u-25)^2]"
> mc-spec clog.circ rgrid-met 5
> dist-circ clog.circ 10 50 5

The dist-spec and mc-spec commands are as before.  The dist-circ
command runs a circularly-coupled simulation from 10 starting points
(the first numerical argument), equally spaced along a 500-iteration
span, so that the sections between starting points consist of 50
iterations (the second numerical argument).  Each section is simulated
and re-simulated up to 5 times (the third numerical argument), with
the starting point the first time (called stage 0) being drawn from
the prior, and the starting points for later stages being set to the
final point from the last simulation of the previous section (with
wrap-around from the last section to the first).

A consistent circular chain is obtained if this process reaches a
state where every section starts at the final state of the previous
section, and ends at the initial state of the next section.  If this
happens after a number of stages that's no more than half the total
number, there is fairly good reason to think that the necessary
assumptions are met, and the states of the circular chain (which will
be in clog.circ) all come from a good approximation to the correct
posterior distribution.

If the -p option is given to dist-circ, the simulation will be done in
parallel, in so far as this is possible with the number of processors