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.
EKF-SLAM Simulator
This version of the simulator uses global variables for
all large objects, such as the state covariance matrix.
While bad programming practice, it is a necessary evil
for MatLab efficiency, as MatLab has no facility to avoid
gratuitous memory allocation and copying when passing
(and modifying) variables between functions. With this
concession, effort has been made to keep the code as
clean and modular as possible.
A Web Tutorial on Discrete Features of Bayes Decision Theory
This applet allows for the calculation of the decision boundary given a three dimensional feature vector. Specifically, by stipulating the variables such as the priors, and the conditional likelihoods of each feature with respect to each class, the changing decision boundary will be displayed.
Proceedings of Practice of Interesting Algorithms 2007
The editor assumes no responsibility for the accuracy, completeness or usefulness of
the information disclosed in this volume. Unauthorized use might infringe on
privately owned patents of publication right. Please contact the individual authors for
permission to reprint or otherwise use information from their papers.
First edition 2007
Publication Planned by Prof. Wenxin Li
Edited by Yili Zhao
All rights reserved
by
Artificial Intelligence Laboratory, Peking University
June 26, 2007
Under the labor sentiment monitor system has several parts of compositions: The mobile termination software and hardware development, the central server software development as well as opens video frequency the prompt reflection. The labor sentiment monitor end is responsible for the data and the scene picture which
