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using MONTE Carlo integeration calculate Q function
標(biāo)簽： integeration calculate function using
上傳時(shí)間： 2013-12-25
上傳用戶：曹云鵬
使用pMatlab改寫B(tài)PSK和QPSK 的MONTE Carlo 仿真程序。在多核PC上實(shí)現(xiàn)MC仿真速度翻倍（附原程序）
標(biāo)簽： pMatlab MONTE Carlo BPSK
上傳時(shí)間： 2016-02-06
上傳用戶：515414293
陣列信號(hào)處理:DML和ULA的MONTE-Carlo仿真
標(biāo)簽： MONTE-Carlo DML ULA 陣列信號(hào)處理
上傳時(shí)間： 2016-02-17
上傳用戶：陽光少年2016
MONTE Garlo 仿真程序，關(guān)于誤碼率與信噪比的對(duì)應(yīng)關(guān)系，希望對(duì)大家學(xué)習(xí)有所幫助
上傳時(shí)間： 2016-02-19
上傳用戶：清風(fēng)冷雨
MONTE Carlo 法是用來解決數(shù)學(xué)和物理問題的非確定性的（概率統(tǒng)計(jì)的或隨機(jī)的）數(shù)值方法。MONTE Carlo 方法（MCM），也稱為統(tǒng)計(jì)試驗(yàn)方法.主要是研究均勻介質(zhì)的穩(wěn)定狀態(tài)[1]。它是用一系列隨機(jī)數(shù)來近似解決問題的一種方法，是通過尋找一個(gè)概率統(tǒng)計(jì)的相似體并用實(shí)驗(yàn)取樣過程來獲得該相似體的近似解的處理數(shù)學(xué)問題的一種手段。
上傳時(shí)間： 2016-02-20
上傳用戶：heart520beat
A MONTE-Carlo Maplet for the Study of the Optical Properties of Biological Tissues
標(biāo)簽： MONTE-Carlo Biological Properties the
上傳時(shí)間： 2014-01-09
上傳用戶：liansi
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
這是MONTE carlo粒子濾波的一個(gè)實(shí)例程序，對(duì)于學(xué)習(xí)卡爾曼濾波和粒子濾波都有很大幫助
標(biāo)簽： MONTE carlo 粒子濾波 實(shí)例程序
上傳時(shí)間： 2016-04-10
上傳用戶：s363994250
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
MONTE carlo 仿真英文電子書 AGuidetoMONTECarloSimulationsinStatisticalPhysics,Second EditionThis new and updated deals with all aspects of MONTE Carlo simulation ofcomplexphysicalsystemsencounteredincondensed-matterphysicsandsta-tistical mechanics as well as in related ?elds, for example polymer science,lattice gauge theory and protein folding
標(biāo)簽： AGuidetoMONTECarloSimulationsinSt atisticalPhysics EditionThis Second
上傳時(shí)間： 2016-04-25
上傳用戶：xmsmh
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