Pseudo Driver Test Demo BufferedIO
标签： BufferedIO Pseudo Driver Demo
上传时间： 2016-04-06
上传用户：tuilp1a
Pseudo Driver Test Demo Direct IO
上传时间： 2016-04-06
上传用户：wangchong
Pseudo Driver Test Demo I/O control
标签： control Pseudo Driver Demo
上传时间： 2016-04-06
上传用户：思琦琦
Pseudo Driver Test Demo ShareFiles Basic
标签： ShareFiles Pseudo Driver Basic
上传时间： 2014-12-02
上传用户：wlcaption
测试stc89C58单片机 测试stc89C58单片机 测试stc89C58单片机DEMO 程序
上传时间： 2014-01-06
上传用户：hj_18
BOOSTING DEMO, A VERY USEFUL DEMO FOR ADABOOST
标签： DEMO BOOSTING ADABOOST USEFUL
上传时间： 2013-12-08
上传用户：chenxichenyue
AT91SAM7S64 demo source code
上传时间： 2013-12-14
上传用户：tonyshao
SUNPLUS(凌阳）GPC1XX demo程序，6502指令
上传时间： 2013-12-23
上传用户：lmeeworm
n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
标签： Rao-Blackwellised conditional filtering particle
上传时间： 2013-12-17
上传用户：zhaiyanzhong
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
标签： demonstrates sequential Selection Bayesian
上传时间： 2016-04-07
上传用户：lindor
