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。
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.
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
