Libgist is an implementation of the Generalized Search Tree, a template index structure that makes it easy to implement any type of Hierarchical access method (AM).
initial working phase of the design of said editor,
featuring multicasting, advanced linux keyboard handling,
sub-Hierarchical expansion, and multiple cursors (similar
to the concept found in moonedit). The author respectfully requests your compliance with the GPL
Quantum Platform(QP) is a family of very lightweight, state machine-based frameworks for embedded systems. QP enables developing well-structured embedded applications as a set of concurrently executing Hierarchical state machines (UML statecharts).
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a Hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
