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
标签： Semiparametric estimation parameters estimates
上传时间： 2016-04-15
上传用户：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
标签： Semiparametric estimation parameters estimates
上传时间： 2016-04-15
上传用户：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
标签： Semiparametric estimation parameters estimates
上传时间： 2014-12-08
上传用户：一诺88
Sequential Monte Carlo without Likelihoods 粒子滤波不用似然函数的情况下 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.
标签： Likelihoods Sequential Bayesian without
上传时间： 2016-05-26
上传用户：离殇
粒子滤波的基本程序及粒子滤波原始论文Novel approach to nonlinear_non-Gaussian Bayesian state estimation
标签： nonlinear_non-Gaussian estimation Bayesian approach
上传时间： 2016-12-07
上传用户：lyy1234
structure EM算法 Bayesian network structure learning
标签： structure Bayesian learning network
上传时间： 2013-11-27
上传用户：ynsnjs
Bayesian network structrue learning matlab program
标签： structrue Bayesian learning network
上传时间： 2016-12-29
上传用户：daguda
Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors.
标签： Variational Multinomial Regression Bayesian
上传时间： 2014-01-11
上传用户：TF2015
A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces
标签： Differential Evolution algorithm Bayesian
上传时间： 2014-01-20
上传用户：hphh
Bayesian Compressed Sensing
标签： Compressed Bayesian Sensing
上传时间： 2014-12-07
上传用户：wuyuying
