Search - EM algorithm for t-distribution

Search - EM algorithm for t-distribution - List

[Mathimatics-Numerical algorithms] RPEM_Source_Code DL : 0: EM算法，基于期望最大化原则进行密度估计-EM algorithm, based on the expectation maximization of the principle of density estimation
Update : 2025-02-17 Size : 3kb Publisher : 丁宏锴
[Data structs] EM-algorithm DL : 0: 这篇文章介绍了EM算法，并且提出了一种加速算法，很不错-This article introduced the EM algorithm, and a speed up the algorithm, very good
Update : 2025-02-17 Size : 1.28mb Publisher : guoguo
[Special Effects] EMyu DL : 0: 最近在做毕设，是有关高斯混合模型的算法，主要采用EM算法，这片硕士论文在这方面介绍的比较详细，可以去下载研究下。-Recently completed the set up to do, is the Gaussian mixture model algorithm, the main use of EM algorithm, this Master
Update : 2025-02-17 Size : 1.72mb Publisher :
[AI-NN-PR] EM DL : 0: EM算法详解！！ EM算法详解-Detailed EM algorithm! ! Detailed EM algorithm
Update : 2025-02-17 Size : 172kb Publisher : sean
[matlab] fit_mix_2D_gaussian DL : 0: fit_mix_2D_gaussian - fit parameters for a 2D mixed-gaussian distribution using EM algorithm format: [u,covar,t,iter] = fit_mix_2D_gaussian( X,M ) input: X - input samples, Nx2 vector M - number of gaussians which are assumed to compose the distribution output: u - fitted mean for each gaussian (each mean is a 2x1 vector) covar - fitted covariance for each gaussian. this is a 2x2xM matrix. t - probability of each gaussian in the complete distribution iter - number of iterations done by the function-fit_mix_2D_gaussian - fit parameters for a 2D mixed-gaussian distribution using EM algorithm format: [u,covar,t,iter] = fit_mix_2D_gaussian( X,M ) input: X - input samples, Nx2 vector M - number of gaussians which are assumed to compose the distribution output: u - fitted mean for each gaussian (each mean is a 2x1 vector) covar - fitted covariance for each gaussian. this is a 2x2xM matrix. t - probability of each gaussian in the complete distribution iter - number of iterations done by the function
Update : 2025-02-17 Size : 2kb Publisher : resident e
[matlab] fit_mix_gaussian DL : 0: 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 distribution output: u - fitted mean for each gaussian sig - fitted standard deviation for each gaussian t - probability of each gaussian in the complete distribution iter- number of iterations done by the function- 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 distribution output: u - fitted mean for each gaussian sig - fitted standard deviation for each gaussian t - probability of each gaussian in the complete distribution iter- number of iterations done by the function
Update : 2025-02-17 Size : 1kb Publisher : resident e