Design/CPN is a graphical tool supporting the use of Hierarchical Timed Coloured Petri Nets. The Editor supports construction, modification, and syntax check of CPN models. The Simulator supports interactive and automatic simulation of CPN models. The Occurrence Graph Tool supports construction and analysis of occurrence graphs for CPN models (also known as state spaces or reachability graphs). The Perfomance Tool supports simulation-based performance analysis of CPN models.
Bayesian observations and applications
1. I noticed a steady drop-off in the performance of my bogofilter after a period of excellent performance.
After some experimentation I had to conclude that my problem was with using too much email for training. (Would it be correct to refer to this as over-training?)
I decided to freshen up my email, and train bogofilter using only the most recent 6 months worth (and I wrote a script to automate and maintain this window).
Performance has gone to excellent again.
2. I am experimenting with using bayesian filtering for general mailbox direction. Instead of training with the classes "spam" and "non-spam" I am also using my work, mailing lists, domain-rego etc mailboxes to train so that the filter can redirect mail into those boxes too.
There are probably a wide number of tasks that can benefit from this approach.
Plain bogofilter a simple effective alternative
Rather than upgrade spamassassin again, I replaced it with bogofilter alone.
After a few thousand training emails it now outperforms that version of spamassassin (which admittedly was ageing). I expect performance to improve further with ongoing training.
Local email aliases allow users on my system to maintain personal bogofilter databases.
I appreciate that recent versions of spamassassin have bayesian learning and that bogofilter can be trained using spamassassin output, but see little reason to complicate an already-effective and elegant solution.