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: Package source: sbgcop_0.95.tar.gz MacOS X binary: sbgcop_0.95.tgz Windows binary: sbgcop_0.95.zip Reference manual: sbgcop.pdf
標簽： Semiparametric estimation parameters estimates
上傳時間： 2016-04-15
上傳用戶：talenthn
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
Sequential Monte Carlo without Likelihoods 粒子濾波不用似然函數的情況下 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.
標簽： Likelihoods Sequential Bayesian without
上傳時間： 2016-05-26
上傳用戶：離殤
粒子濾波的基本程序及粒子濾波原始論文Novel approach to nonlinear_non-Gaussian Bayesian state estimation
標簽： nonlinear_non-Gaussian estimation Bayesian approach
上傳時間： 2016-12-07
上傳用戶：lyy1234
structure EM算法 Bayesian network structure learning
標簽： structure Bayesian learning network
上傳時間： 2013-11-27
上傳用戶：ynsnjs
Bayesian network structrue learning matlab program
標簽： structrue Bayesian learning network
上傳時間： 2016-12-29
上傳用戶：daguda
Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors.
標簽： Variational Multinomial Regression Bayesian
上傳時間： 2014-01-11
上傳用戶：TF2015
A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces
標簽： Differential Evolution algorithm Bayesian
上傳時間： 2014-01-20
上傳用戶：hphh
Bayesian Compressed Sensing
標簽： Compressed Bayesian Sensing
上傳時間： 2014-12-07
上傳用戶：wuyuying
