This a bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
bayeserr - Computes the bayesian risk for optimal classifier.
％ bayescln - Classifier based on Bayes decision rule for Gaussians.
％ bayesnd - Discrim. function, dichotomy, max aposteriori probability.
％ bhattach - Bhattacharya s upper limit of mean class. error.
％ pbayescln - Plots discriminat function of Bayes classifier.
ReBEL is a Matlabtoolkit of functions and scripts, designed to
facilitate sequential bayesian inference (estimation) in general state
space models. This software consolidates research on new methods for
recursive bayesian estimation and Kalman filtering by Rudolph van der
Merwe and Eric A. Wan. The code is developed and maintained by Rudolph
van der Merwe at the OGI School of Science & Engineering at OHSU
(Oregon Health & Science University).
a Java toolkit for training, testing, and applying bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.
The bayesian Committee Machine (BCM) is an approximation method for
large-scale Gaussian process regression.
- The code is for Matlab
Version 1.0, November 2005
該程序?yàn)榛诹Ｗ訛V波的一種新算法,綜合MCMC bayesian Model Selection即MONTE CARLO馬爾克夫鏈的算法,用來實(shí)現(xiàn)目標(biāo)跟蹤,多目標(biāo)跟蹤,及視頻目標(biāo)跟蹤及定位等,解決非線性問題的能力比卡爾曼濾波,EKF,UKF好多了,是我珍藏的好東西,現(xiàn)拿出來與大家共享,舍不得孩子套不著狼,希望大家相互支持,共同促進(jìn).
