sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Windows binary: sbgcop_0.95.zip
標簽： Semiparametric estimation parameters estimates
上傳時間： 2016-04-15
上傳用戶：qilin
sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Reference manual: sbgcop.pdf
標簽： Semiparametric estimation parameters estimates
上傳時間： 2014-12-08
上傳用戶：一諾88
Markov分析的matlab工具包，包含Markov回歸分析等內容
上傳時間： 2016-04-21
上傳用戶：er1219
Creates a Gaussian mixture model with specified architecture.MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space DIM, the number of centres in the mixture model and the type of the mixture model, and returns a data structure MIX.
標簽： architecture COVARTYPE specified Gaussian
上傳時間： 2016-04-28
上傳用戶：dyctj
Generate the digital AWGN signal n[k] (sampled n(t)) by generating zero mean Gaussian random variables independently (separately) for each k MATLAB function random.
標簽： generating Generate Gaussian digital
上傳時間： 2014-01-15
上傳用戶：sammi
The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants. The functions (m-functions) were developped with MATLAB v6.0 (one of the functions requires the Mathworks Optimization Toolbox) by the decision team of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France). The version 2.0 (February 2005) handles sparse matrices and contains an example
標簽： discrete-time resolution functions Decision
上傳時間： 2014-01-01
上傳用戶：xuanjie
％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％
標簽： multidimensional estimation algorithm Gaussian
上傳時間： 2013-12-03
上傳用戶：我們的船長
This m file models a UWB system using BPSK with the fifth order derivative of the gaussian pulse with correlation receiver and intgrator.
標簽： derivative the gaussian models
上傳時間： 2016-06-28
上傳用戶：xuanjie
Multiple alignment using hidden Markov models
標簽： alignment Multiple Markov hidden
上傳時間： 2016-07-08
上傳用戶：gundamwzc
General Hidden Markov Model Library 一個通用的隱馬爾科夫模型的C代碼庫
標簽： General Library Hidden Markov
上傳時間： 2014-01-14
上傳用戶：ardager
