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Title: IGMM Download
 Description: Dirichlet process based on the genetic signal processing method
 Downloaders recently: [More information of uploader luoluo.1988]
 To Search: iGMM Dirichlet
  • [iHMM] - infinite HMMRefer to Beal s PaperApplyin
  • [GN] - A genetic algorithm for image or data in
  • [IGMM] - Gaussian model of Gaussian model
  • [featureselection] - feature selection with genetic algorithm
  • [Bayesian] - this is the doc related to the Bayesian
  • [IGMM] - Multi-Gaussian model is the detection of
  • [vdpgm.tar] - Variational Dirichlet Process Gaussian M
  • [IGMM] - Gaussian mixture-based moving object det
  • [hmmbox_4_1] - the newer version from HMMbox 3.2 Matlab
File list (Check if you may need any files):
crp\lp_crp.m
...\lpnormalinvwish.m
...\plot_mixture.m
...\sampler.m
...\test_sampler.m
distributions\@distribution
.............\.............\distribution.m
.............\.............\lp.m
.............\.............\p.m
.............\.............\plot.m
.............\@gaussian
.............\.........\char.m
.............\.........\dimension.m
.............\.........\display.m
.............\.........\fit.m
.............\.........\gaussian.m
.............\.........\get.m
.............\.........\length.m
.............\.........\lp.m
.............\.........\multinomial.m
.............\.........\p.m
.............\.........\plus.m
.............\.........\private
.............\.........\.......\fvnlp.m
.............\.........\sample.m
.............\.........\set.m
.............\.........\subsasgn.m
.............\.........\subsref.m
.............\@gaussian_mixture_model
.............\.......................\gaussian_mixture_model.m
.............\.......................\plot.m
.............\.......................\train.m
.............\@linear_gaussian_conditional
.............\............................\char.m
.............\............................\dimension.m
.............\............................\display.m
.............\............................\fit.m
.............\............................\get.m
.............\............................\linear_gaussian_conditional.m
.............\............................\lp.m
.............\............................\p.m
.............\............................\plus.m
.............\............................\private
.............\............................\.......\fvnlp.m
.............\............................\sample.m
.............\............................\set.m
.............\@mixture_model
.............\..............\char.m
.............\..............\display.m
.............\..............\fit.m
.............\..............\get.m
.............\..............\lp.m
.............\..............\mixture_model.m
.............\..............\p.m
.............\..............\plus.m
.............\..............\sample.m
.............\..............\set.m
.............\..............\subsref.m
.............\@multinomial
.............\............\char.m
.............\............\display.m
.............\............\fit.m
.............\............\get.m
.............\............\length.m
.............\............\lp.m
.............\............\multinomial.m
.............\............\p.m
.............\............\private
.............\............\.......\fvnlp.m
.............\............\.......\msum.m
.............\............\sample.m
.............\............\set.m
.............\............\size.m
.............\............\subsasgn.m
.............\............\subsref.m
utilities\fvgp.m
.........\fvnlp.m
.........\fvnp.m
.........\fvplp.m
.........\fvpp.m
.........\init_logger.m
.........\logger_close.m
.........\logger_init.m
.........\logger_record.m
README.txt
    

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