Strategy for doing number-crunching

We have a database with approximately 130M rows, and we need to produce
statistics (e.g. mean, standard deviation, etc.) on the data. Right now,
we're generating these stats via a single SELECT, and it is extremely
slow...like it can take hours to return results. The queries are variable,
so there is no way I can think of to "pre-calculate" the stats.

Here is an example query...

SELECT count(mae) AS count, to_char(avg(mae), 'FM990.999') AS mae,
to_char(avg(sqrt(rms)), 'FM990.999') AS rms, to_char(avg(bias),
'FM990.999') AS bias,
to_char(max(mae), 'FM990.999') AS mae_max, to_char(min(mae), 'FM990.999')
AS mae_min,
to_char(max(bias), 'FM990.999') AS bias_max, to_char(min(bias),
'FM990.999') AS bias_min,
to_char(max(sqrt(rms)), 'FM990.999') AS rms_max, to_char(min(sqrt(rms)),
'FM990.999') AS rms_min,
to_char(stddev(sqrt(rms)), 'FM990.999') AS rms_stddev,
to_char(stddev(mae), 'FM990.999') AS mae_stddev,
to_char(stddev(bias), 'FM990.999') AS bias_stddev
FROM verify_data.verification_data
WHERE model_name='foo'
AND...several other conditions...

As one might imagine, this query pounds the CPU relentlessly for hours.

I'm thinking there must be a better approach for doing large amounts of
calculations on the data. Any pointers would be greatly appreciated!

Matt