project5_4main

Statistical Methods and Applications (SMA) is the official Journal of the Italian Statistical Society. This international journal fosters the development of statistical methodology and its applications in biological, demographic, economic, health, physical, social, and other scientific domains. In particular, the journal emphasizes investigations of methodological foundations and methods that have broad applications.

MATLAB下的瑞利多径信道仿真，经过验证，可以使用(MATLAB under the multipath Rayleigh channel simulation, after verification, you can use)

说明：  harris 角点检测的完整matlab程序 可直接运行(harris corner detection of the complete program can be run matlab)

说明：  基于matlab的pcm系统的仿真之对语音信号的均匀量化编码(Matlab-based system simulation of the pcm speech signal uniform quantization coding)

多维标度变换是应用广泛的无线传感器网络定位算法(Multi-Dimensional Scaling)

Capacity_and_Channel for mimo channel

简单易懂的matlab较新版本的教程，深入浅出的讲解matlab的代码编写方法。(Straightforward newer version of matlab tutorial, explain in simple terms matlab coding method.)

有用的MATLAB小程序，和大家交流一下，下网在这里可以学到很多(useful)

这是另一个相关程序，求解传热系数的基本思想相同，只是温度的检测点不同。(This is another procedure, solving heat transfer coefficient of the same basic idea, but the detection of temperature difference.)

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. (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. )