using Monte Carlo integeration calculate Q function
標簽： integeration calculate function using
上傳時間： 2013-12-25
上傳用戶：曹云鵬
使用pMatlab改寫BPSK和QPSK 的Monte Carlo 仿真程序。在多核PC上實現MC仿真速度翻倍（附原程序）
上傳時間： 2016-02-06
上傳用戶：515414293
陣列信號處理:DML和ULA的MONTE-Carlo仿真
標簽： MONTE-Carlo DML ULA 陣列信號處理
上傳時間： 2016-02-17
上傳用戶：陽光少年2016
Monte Carlo 法是用來解決數學和物理問題的非確定性的（概率統計的或隨機的）數值方法。Monte Carlo 方法（MCM），也稱為統計試驗方法.主要是研究均勻介質的穩定狀態[1]。它是用一系列隨機數來近似解決問題的一種方法，是通過尋找一個概率統計的相似體并用實驗取樣過程來獲得該相似體的近似解的處理數學問題的一種手段。
上傳時間： 2016-02-20
上傳用戶：heart520beat
A MONTE-Carlo Maplet for the Study of the Optical Properties of Biological Tissues
標簽： MONTE-Carlo Biological Properties the
上傳時間： 2014-01-09
上傳用戶：liansi
這是monte carlo粒子濾波的一個實例程序，對于學習卡爾曼濾波和粒子濾波都有很大幫助
上傳時間： 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.
標簽： sequential reversible algorithm nstrates
上傳時間： 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
標簽： AGuidetoMonteCarloSimulationsinSt atisticalPhysics EditionThis Second
上傳時間： 2016-04-25
上傳用戶：xmsmh
Hybrid Monte Carlo sampling.SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo algorithm to sample from the distribution P ~ EXP(-F), where F is the first argument to HMC. The Markov chain starts at the point X, and the function GRADF is the gradient of the `energy function F.
標簽： Carlo Monte algorithm sampling
上傳時間： 2013-12-02
上傳用戶：jkhjkh1982
Sequential Monte Carlo without Likelihoods 粒子濾波不用似然函數的情況下 本文摘要:Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection sampling or Markov chain Monte Carlo can be highly inefficient, and accordingly require far more iterations than may be practical to implement. Here we propose a sequential Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study of the transmission rate of tuberculosis.
標簽： Likelihoods Sequential Bayesian without
上傳時間： 2016-05-26
上傳用戶：離殤
