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MODELING PROBABILITIES FOR CATEGORICAL DATA

As an example of a model for categorical data, I will show here how to
model probabilities for targets that come from the set {0,1,2}, with
the prior distribution for these probabilities being uniform over the
simplex of valid probabilities.  This example illustrates one way of
representing probabilities using unbounded real parameters.  It can
easily be generalized to arbitrary Dirichlet priors.  I also show how
to compute the marginal likelihood for this model by using Annealed
Importance Sampling, and also by using simple importance sample (which
can be done as a degenerate case of AIS).

Specifying the model.

The probabilities for the three possible values of the target will be
represented using the parameters w0, w1, and w2, with the probability
of the value "0" being Exp(w0)/[Exp(w0)+Exp(w1)+Exp(w2)] and similarly
for the other values.  These probabilities will be positive and sum to
one for any values of the parameters.  This is necessary if we are to
use this software, since it doesn't allow state variables to be
constrained.

With this representation, the model can be specified by the following
somewhat formidable command:

> dist-spec plog.met \
"w0~ExpGamma(1,1) + w1~ExpGamma(1,1) + w2~ExpGamma(1,1)" \
"-Delta(t-0)*(w0-LogSumExp(w0,w1,w2)) \
-Delta(t-1)*(w1-LogSumExp(w0,w1,w2)) \
-Delta(t-2)*(w2-LogSumExp(w0,w1,w2))"

These priors for w0, w1, and w2 will produce a uniform prior for the
resulting probabilities.  More generally, if the priors for w0, w1,
etc. are ExpGamma(a1,1), ExpGamma(a2,1), etc., the prior distribution
for the probabilities Exp(w0)/[Exp(w0)+...], Exp(w1)/[Exp(w0)+...],
and so forth will be Dirichlet with parameters a1, a2, etc.

The terms in the likelihood are constructed using the "Delta" function
which has the value one when its argument is zero and has the value
zero otherwise.  Since the target, t, is in the set {0,1,2}, exactly
one of the terms in the likelihood will be non-zero for each case.
When the target has the value "0", the likelihood should be minus the
log of the probability of the target being "0", that is:

- Log { Exp(w0) / [ Exp(w0) + Exp(w1) + Exp(w2) ] }

In the specification above, this is written as

- (w0 - LogSumExp(w0,w1,w2))

using the "LogSumExp" function, which computes the log of the sum of
the exponentials of its arguments.  This is shorter and faster.  More
importantly, the LogSumExp function is guaranteed to produce a valid
result even when computing Exp(w0), Exp(w1), or Exp(w2) would result
in floating point overflow or underflow.  This is probably not crucial
for this example, but it is important in some similar contexts.

Specifying the data source.

The data is stored one item per line in the file "pdata".  This source
is specified with the following command:

> data-spec plog.met 0 1 3 / pdata .

The arguments after the log file are the number of input variables
(0), the number of target variables (1), and an argument (3) that
indicates that the targets are categorical with three possible values,
represented by the integers 0, 1, and 2.  This argument could have
been left out, but including it causes the data to be checked to
ensure that each item is in the set {0,1,2}.

zThe remaining arguments give the files where the inputs (all zero of
them) and targets come from, with the "." for the latter indicating
that the targets come from the same place as the inputs.

The file "pdata" contains 17 items, of which 6 are "0", 8 are "1", and
3 are "2".

Sampling with the Metropolis algorithm.

We can sample from the posterior distribution using any of various
Markov chain methods.  Here's one example using the Metropolis
algorithm:

> mc-spec plog.met repeat 10 metropolis 1
> dist-mc plog.met 1000

This takes about eight seconds on our machine.  Convergence is quite
rapid, and the sampling efficiency is reasonably good.

We can estimate the probabilities of the three possible values as
follows:

> dist-est "Exp(w0)/[Exp(w0)+Exp(w1)+Exp(w2)]" plog.met 100:

Number of sample points: 901

Estimates for Exp(w0)/[Exp(w0)+Exp(w1)+Exp(w2)]:

Mean:    0.349174  (standard error 0.00342273)
Std.dev: 0.102739

NOTE: The standard errors assume points are independent

> dist-est "Exp(w1)/[Exp(w0)+Exp(w1)+Exp(w2)]" plog.met 100:

Number of sample points: 901

Estimates for Exp(w1)/[Exp(w0)+Exp(w1)+Exp(w2)]:

Mean:    0.450742  (standard error 0.00354029)
Std.dev: 0.106268

NOTE: The standard errors assume points are independent

> dist-est "Exp(w2)/[Exp(w0)+Exp(w1)+Exp(w2)]" plog.met 100:

Number of sample points: 901

Estimates for Exp(w2)/[Exp(w0)+Exp(w1)+Exp(w2)]:

Mean:    0.200084  (standard error 0.00290151)
Std.dev: 0.0870937

NOTE: The standard errors assume points are independent

Note that since the points are not entirely independent, the standard
errors are not quite right.  For comparison, the exactly correct
posterior means of the three probabilities (found analytically) are
0.35, 0.45, and 0.2.

Computing the marginal likelihood with Annealed Importance Sampling.

We can use Annealed Importance Sampling (AIS) to find the prior
probability of the observed data given this model - sometimes called
the "marginal likelihood".  This is the normalizing constant for the
posterior distribution, if it is specified as the product of the prior
and the likelihood.  Annealed Importance Sampling (which is described
in a technical report of this name available from my web page) can
also help in handling isolated modes, though this should not be a
problem for this model.

To use AIS, we specify the model and the data source as above, and
then use the following commands:

> mc-temp-sched plog.ais 0.1 0.3 0.6
> mc-spec plog.ais AIS repeat 10 metropolis 0.5
> dist-mc plog.ais 1000

The 'mc-temp-sched' command specifies a "tempering schedule": a series
of distributions that the annealing run will pass through.  For a
Bayesian model, these distributions are created by multiplying the log
likelihood part of the "energy" by various "inverse temperatures",
listed as arguments to 'mc-temp-sched'.  A final distribution with
inverse temperature of 1 is assumed at the end of the schedule.  This
final distribution is the posterior, which we wish to sample from, and
to find the normalizing constant for.

The chain proceeds sequentially through these distributions.  The
"AIS" operation moves to the next distribution in the schedule, and
updates a "weight" according to the ratio of probabilities under the
new and old distributions.  If the chain is at the last distribution
(at inverse temperature 1), it moves to the first distribution (here,
at inverse temperature 0.1), after first drawing a new state from the
distribution at inverse temperature 0, which for a Bayesian model is
the prior.  There are four temperatures in the tempering schedule
here, and each iteration does one "AIS" operation.  The 1000
iterations will therefore produce 250 annealing runs.

Markov chain operations such as "metropolis" operate as usual, except
that the "energy function" is modified by the inverse temperature.

For AIS to operate, it must be possible to sample from the prior.  If
the prior is specified entirely as a sum of "~" terms, with no
circular references, the software knows how to sample from it.
Otherwise, you must provide a separate program to do the sampling.
For how this is done, see the documentation on the -read-prior option
in dist-spec.doc and also the information in dist-mc.doc.

The commands above take about nine seconds on our machine.  Once they
have finished, we can estimate functions of state with 'dist-est',
which will automatically look at only the iterations that pertain to
the final distribution of interest.  It also computes weighted
estimates using the weights found during the annealing runs.  Here is
an example:

> dist-est "Exp(w0)/[Exp(w0)+Exp(w1)+Exp(w2)]" plog.ais

Number of sample points: 250

Variance of normalized weights: 1.21148
Adjusted sample size: 113.0  (reduction factor 0.452)

Estimates for importance weights:

Mean of weights: 2.85948e-09  (standard error 1.99056e-10)
Log of mean:    -19.6726  (standard error 0.0696127)

Standard estimates for Exp(w0)/[Exp(w0)+Exp(w1)+Exp(w2)]:

Mean:    0.342344  (standard error 0.00925806)
Std.dev: 0.100527

Effective sample size: 117.9  (reduction factor 0.472)

Jacknife estimates for Exp(w0)/[Exp(w0)+Exp(w1)+Exp(w2)]:

Mean:    0.342353  (standard error 0.00934127)

Effective sample size: 115.8  (reduction factor 0.463)

NOTE: The standard errors assume points are independent

Note that there is no need to discard any of the early iterations,
since each annealing run is independent.  Because of this
independence, the standard errors computed are valid, subject to the
usual caveats regarding the possibility that some important part of
the distribution has been missed entirely.

Along with an estimate for expectation of the specified function,
'dist-est' outputs an estimate for the mean of the weights, which for
a Bayesian model will be the marginal likelihood, provided the
likelihood specification included all terms, even those that are
constant.  The log of this estimate is also output, since the value
will often be extremely small or large.

For this model, the marginal likelihood can be computed exactly by
multiplying successive predictive probabilities.  For this data, it is

6! 8! 3!
--------  =  2.86378e-9    log is -19.67112
19! / 2!

The estimates and estimated standard errors are consistent with this.

This problem is actually easy enough that the marginal likelihood can
be found by simple importance sampling.  This can be viewed as a
degenerate form of Annealed Importance Sampling in which the tempering
schedule has only one distribution - the assumed one, at temperature
1, which is the posterior.  We can do simple importance sampling with
the following commands:

> mc-temp-sched plog.is -
> mc-spec plog.is AIS
> dist-mc plog.is 1000

The 'mc-temp-sched' command with the argument "-" sets up the null
tempering schedule.  The 'mc-spec' command just has an "AIS"
operation, with no Markov chain operations.  This causes a new state
to be drawn from the prior every iteration, and the appropriate weight
to be computed based on the likelihood.

Estimates for expectations and for the marginal likelihood can be
found using 'dist-est'.  For example:

> dist-est "Exp(w0)/[Exp(w0)+Exp(w1)+Exp(w2)]" plog.is

Number of sample points: 1000

Variance of normalized weights: 3.85409
Adjusted sample size: 206.0  (reduction factor 0.206)

Estimates for importance weights:

Mean of weights: 2.83221e-09  (standard error 1.75827e-10)
Log of mean:    -19.6822  (standard error 0.0620814)

Standard estimates for Exp(w0)/[Exp(w0)+Exp(w1)+Exp(w2)]:

Mean:    0.344737  (standard error 0.00530831)
Std.dev: 0.104735

Effective sample size: 389.3  (reduction factor 0.389)

Jacknife estimates for Exp(w0)/[Exp(w0)+Exp(w1)+Exp(w2)]:

Mean:    0.344731  (standard error 0.00532859)

Effective sample size: 386.3  (reduction factor 0.386)

NOTE: The standard errors assume points are independent

Doing importance sampling takes only a second on our machine, which is
faster than Annealed Importance Sampling.  However, importance
sampling quickly becomes infeasible for problems with more parameters
or with more data.  Annealed Importance Sampling is more feasible for
realistic problems, though some work is needed to set up a good
tempering schedule.  See mc-ais.doc for documentation on how to obtain
information that is helpful in doing this.

The software also supports the related methods of simulated tempering,
tempered transitions, and tempered hybrid Monte Carlo.  There's no
tutorial documentation on how to use these methods, though - just the
documentation in mc-spec.doc.

Annealed importance sampling and the tempering schemes are also
supported for neural network models, but not for Gaussian process and
mixture models.
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