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.
標(biāo)簽： demonstrates sequential Selection Bayesian
上傳時(shí)間： 2016-04-07
上傳用戶：lindor
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.
標(biāo)簽： sequential reversible algorithm nstrates
上傳時(shí)間： 2014-01-18
上傳用戶：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.
標(biāo)簽： reversible algorithm the nstrates
上傳時(shí)間： 2014-01-08
上傳用戶：cuibaigao
void III_hufman_decode(struct Granule *gr,int part2_start, int freqline[SBLIMIT][SSLIMIT]) { unsigned int reg1, reg2,i unsigned int part3_length = part2_start + gr->part2_3_length unsigned used int h,*f=&freqline[0][0] if(gr->window_switching_flag && gr->block_type == 2) { /* short block regions */ reg1 = 36 reg2 = 576 } else { /* long block regions */ reg1 = sfBandIndex[fr_ps.header->sampling_frequency].l[gr->region0_count + 1] reg2 = sfBandIndex[fr_ps.header->sampling_frequency].l[gr->region0_count + gr->region1_count + 2] }
標(biāo)簽： III_hufman_decode int freqline Granule
上傳時(shí)間： 2013-12-19
上傳用戶：jjj0202
The SST89E516RDx and SST89V516RDx are members of the FlashFlex51 family of 8-bit microcontroller products designed and manufactured with SST’s patented and proprietary SuperFlash CMOS semiconductor process technology. The split-gate cell design and thick-oxide tunneling injector offer significant cost and reliability benefits for SST’s customers. The devices use the 8051 instruction set and are pin-for-pin compatible with standard 8051 microcontroller devices.
標(biāo)簽： microcontroller SST 516 RDx
上傳時(shí)間： 2014-01-08
上傳用戶：笨小孩
BLDC無(wú)刷電機(jī)的C控制源碼，5部分組成：1.bldc.c 2.ac.c 3.wm.c 4.hsensor.c 5.timer1.c 和a header file (bldc.h)
標(biāo)簽： BLDC 無(wú)刷電機(jī) 控制 源碼
上傳時(shí)間： 2014-11-03
上傳用戶：13160677563
** File name: target.h ** Last modified Date: 2004-09-17 ** Last Version: 1.0 ** Descriptions: header file of the specific codes for LPC2100 target boards ** Every project should include a copy of this file, user may modify it as ne
標(biāo)簽： Last Description modified Version
上傳時(shí)間： 2014-05-30
上傳用戶：wanghui2438
1.本目錄存放了演示自定義標(biāo)簽開(kāi)發(fā)與使用的web應(yīng)用程序，可以直接部署到應(yīng)用服務(wù)器并運(yùn)行。 2.shopping存放了電子商店程序，該程序的header.jsp使用自定義標(biāo)簽顯示當(dāng)前系統(tǒng)日期。 3.tag存放演示傳統(tǒng)標(biāo)簽開(kāi)發(fā)與使用的例程序。 4.simple存放演示簡(jiǎn)單標(biāo)簽開(kāi)發(fā)與使用的例程序。由于WebLogic Server8.1不支持JSP2.0，所以simple程序不能在WebLogic Server8.1下運(yùn)行。
標(biāo)簽： web 目錄 標(biāo)簽 應(yīng)用程序
上傳時(shí)間： 2013-12-17
上傳用戶：yuanyuan123
SOPC實(shí)驗(yàn)--Hello World實(shí)驗(yàn):啟動(dòng)Quartus II軟件，選擇File→New Project Wizard，在出現(xiàn)的對(duì)話框中填寫項(xiàng)目名稱 2、 點(diǎn)擊Finish，然后選擇“是”。選擇Assignments→Device，改寫各項(xiàng)內(nèi)容。Family改為CycloneII，根據(jù)實(shí)驗(yàn)板上的器件選擇相應(yīng)的器件，本實(shí)驗(yàn)選擇EP2C5T144C8,點(diǎn)擊對(duì)話框中的Device & Pin Options，在Configuration中，選項(xiàng)Use Configuration Device為EPCS1，選項(xiàng)Unused Pins為As inputs,tri-stated.
標(biāo)簽： Quartus Hello World SOPC
上傳時(shí)間： 2014-01-13
上傳用戶：梧桐
The Hardware Book contains misc technical information about computers and other electronic devices. You ll find the pin out to most common (and uncommon) connectors availble, as well as info about how to build cables.
標(biāo)簽： information electronic computers technical
上傳時(shí)間： 2013-11-30
上傳用戶：cc1015285075
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