资源列表
[matlab例程] plot3d_2
说明：This function produces an image of a 3D object defined by matrix a(l,m,n) in terms of voxels the image is a view after rotating the object by angles alfa and beta (in degree) b is the image and d is its ditance to the viewer matrix The first figure d<resident e> 在 2025-06-08 上传 | 大小：1kb | 下载：0
[matlab例程] randgen2
说明：randgen(mu,mu1,mu2,cov1,cov2,cov3) = Random generation of Gaussian Samples in d-dimensions where d = 2 mu, mu1, mu2 = (x,y) coordinates(means) that the gaussian samples are centered around cov1, cov2, cov3 are the covariance matrices and will v<resident e> 在 2025-06-08 上传 | 大小：1kb | 下载：0
[matlab例程] fit_mix_gaussian
说明： fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm format: [u,sig,t,iter] = fit_mix_gaussian( X,M ) input: X - input samples, Nx1 vector M - number of gaussians which are assumed to compose the distributi<resident e> 在 2025-06-08 上传 | 大小：1kb | 下载：0
[matlab例程] fit_ML_laplace
说明： fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b)<resident e> 在 2025-06-08 上传 | 大小：1kb | 下载：0
[matlab例程] fit_ML_log_normal
说明： fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b)<resident e> 在 2025-06-08 上传 | 大小：1kb | 下载：0
[matlab例程] fit_ML_maxwell
说明： fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!. Given the samples of a log-normal distribution, the PDF parameter is found fits data to the probability of the form: p(x) = sqrt(1/(2*pi))/(s*x)*<resident e> 在 2025-06-08 上传 | 大小：1kb | 下载：0
[matlab例程] fit_ML_normal
说明： fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!. Given the samples of a normal distribution, the PDF parameter is found fits data to the probability of the form: p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u<resident e> 在 2025-06-08 上传 | 大小：1kb | 下载：0
[matlab例程] fit_ML_rayleigh
说明：fit_ML_rayleigh - Maximum Likelihood fit of the rayleigh distribution of i.i.d. samples!. Given the samples of a rayleigh distribution, the PDF parameter is found fits data to the probability of the form: p(r)=r*exp(-r^2/(2*s))/s wit<resident e> 在 2025-06-08 上传 | 大小：1kb | 下载：0