  In various fields, statistical models of interest are analytically
intractable. As a result, statistical inference is greatly hampered by
computational constraints. However, given a model, different users with
different data are likely to perform similar computations. Computations done by
one user are potentially useful for other users with different data sets. We
propose a pooling of resources across researchers to capitalize on this. More
specifically, we preemptively chart out the entire space of possible model
outcomes in a prepaid database. Using advanced interpolation techniques, any
individual estimation problem can now be solved on the spot. The prepaid method
can easily accommodate different priors as well as constraints on the
parameters. We created prepaid databases for three challenging models and
demonstrate how they can be distributed through an online parameter estimation
service. Our method outperforms state-of-the-art estimation techniques in both
speed (with a 23,000 to 100,000-fold speed up) and accuracy, and is able to
handle previously quasi inestimable models.
