Hello all, I am happy to say that my dissertation is available for your reading pleasure -- a misspelling of ``Parmenidean'' has been corrected. :) You will find .ps, .ps.gz, and .pdf versions on my publications page, http://civil.colorado.edu/~dodier/publications.html >From the point of view of UAI folks, I believe the features of greatest interest are these: (1) A scheme for handling a wider range of conditional distributions -- an attempt is made at run time to determine whether an exact result can be computed and otherwise an approximation is made. Toward this end a catalog of exact results has been accumulated (Appendix C). Approximations are of two sorts -- the mixture of Gaussians which is doubtless familiar to you, and monotone cubic splines, which are perhaps a little less familiar; I've become a big fan of monotone cubic splines. (2) An implementation of distributed belief networks. Belief networks can be spread across multiple Internet hosts and communicate by a message-passing algorithm. Parent variables can be named by host and a belief network on that host. Each host runs software to carry out computations locally and to exchange messages with parents and children on other hosts. I used Java's Remote Method Invocation classes to implement the message-passing; at the bottom are socket connections. I have found that even if all belief networks are running on a single host, a decomposition of a model into a distributed belief network (rather than a monolithic one) can speed model development and make modifications easier. (3) Engineering applications are presented which include sensor fusion, diagnosis of equipment status, assessment of the influence of model variables by mutual information, and analysis of uncertainty in a first-principles model. I have found distributed belief networks to be very effective models in engineering problems, because it is so natural to model different sources of information and fuse them together using the laws of probability. I look forward to further progress in this field! Enjoy, Robert Dodier
