High-speed Interface to Host EPP Parallel Port * Version: 1.0 * Last updated: 2001.12.20 * target: All AVR Devices with 12 I/O pins
标签： High-speed Interface Parallel Version
上传时间： 2013-12-25
上传用户：小草123
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
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
标签： filtering particle Blackwellised conditionall
上传时间： 2014-12-05
上传用户：410805624
In 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： 2013-12-14
上传用户：小儒尼尼奥
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
标签： Rauch-Tung-Striebel algorithm smoother which
上传时间： 2016-04-15
上传用户：zhenyushaw
sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Package source: sbgcop_0.95.tar.gz MacOS X binary: sbgcop_0.95.tgz Windows binary: sbgcop_0.95.zip Reference manual: sbgcop.pdf
标签： Semiparametric estimation parameters estimates
上传时间： 2016-04-15
上传用户：talenthn
WINRAR 是现在最好的压缩工具，界面友好，使用方便，在压缩率和速度方面都有很好的表现。其压缩率比之 WINZIP 之流要高，3.30 增加了病毒扫描等功能。RAR 采用了比 Zip 更先进的压缩算法，是现在压缩率较大、压缩速度较快的格式之一。 主要特点：对 RAR 和 ZIP 的完全支持； 支持 ARJ、CAB、LZH、ACE、tar、GZ、UUE、BZ2、JAR、ISO 类型文件的解压；多卷压缩功能；创建自释放文件，可以制作简单的安装程序，使用方便；强大的档案文件修复功能，最大限度恢复损坏的 rar 和 zip 压缩文件中的数据，如果设置了恢复记录，甚至可能完全恢复等等……（反正是比 WinZIP 好得多，强烈建议使用 WinRAR 替换 Winzip) 注：此版本安装后即为正式注册版，不含任何个人信息，请放心使用
标签： WINRAR
上传时间： 2013-12-12
上传用户：it男一枚
This demo nstrates 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： sequential reversible algorithm nstrates
上传时间： 2014-01-18
上传用户：康郎
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.
标签： reversible algorithm the nstrates
上传时间： 2014-01-08
上传用户：cuibaigao
