using Monte Carlo integeration calculate Q function
标签： integeration calculate function using
上传时间： 2013-12-25
上传用户：曹云鹏
使用pMatlab改写BPSK和QPSK 的Monte Carlo 仿真程序。在多核PC上实现MC仿真速度翻倍（附原程序）
上传时间： 2016-02-06
上传用户：515414293
阵列信号处理:DML和ULA的Monte-carlo仿真
标签： Monte-carlo DML ULA 阵列信号处理
上传时间： 2016-02-17
上传用户：阳光少年2016
Monte Carlo 法是用来解决数学和物理问题的非确定性的（概率统计的或随机的）数值方法。Monte Carlo 方法（MCM），也称为统计试验方法.主要是研究均匀介质的稳定状态[1]。它是用一系列随机数来近似解决问题的一种方法，是通过寻找一个概率统计的相似体并用实验取样过程来获得该相似体的近似解的处理数学问题的一种手段。
上传时间： 2016-02-20
上传用户：heart520beat
A Monte-carlo Maplet for the Study of the Optical Properties of Biological Tissues
标签： Monte-carlo Biological Properties the
上传时间： 2014-01-09
上传用户：liansi
这是monte carlo粒子滤波的一个实例程序，对于学习卡尔曼滤波和粒子滤波都有很大帮助
上传时间： 2016-04-10
上传用户：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.
标签： sequential reversible algorithm nstrates
上传时间： 2014-01-18
上传用户：康郎
monte carlo 仿真英文电子书 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
标签： AGuidetoMonteCarloSimulationsinSt atisticalPhysics EditionThis Second
上传时间： 2016-04-25
上传用户：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.
标签： Carlo Monte algorithm sampling
上传时间： 2013-12-02
上传用户：jkhjkh1982
Sequential Monte Carlo without Likelihoods 粒子滤波不用似然函数的情况下 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.
标签： Likelihoods Sequential Bayesian without
上传时间： 2016-05-26
上传用户：离殇
