OpenSS7 This the fourth public release of the OpenSS7 Master Package. See README in the release for a sub-package listing. Most of the sub-packages in the release are production grade for Linux Fast-STREAMS. All existing validation test suites run clean on supported Distributions and architectures.
It is unlikely that the OpenSS7 Master Package will be released as frequently as before. Sub-packages will be released more often. To rebuild the master package with a new sub-package release, simply replace the directory to which the sub-package belongs with the unpacked sub-package release and then rebuild the master package. This release provides support for recent Distributions and tool chains.
With User Mode Linux you can create virtual Linux machines within a Linux computer and use them to safely test and debug applications, network services, and even kernels. You can try out new Distributions, experiment with buggy software, and even test security. Now, for the first time, the creator and maintainer of User Mode Linux shows how to put it to work hands-on. Jeff Dike covers everything from getting started through running enterprise-class User Mode Linux servers. You ll find authoritative advice on bootup, compilation, administration, specialized configurations, and much more.
In this article, we present an overview of methods for sequential simulation from posterior Distributions.
These methods are of particular interest in Bayesian filtering for discrete time dynamic models
that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed
that unifies many of the methods which have been proposed over the last few decades in several
different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin
particular how to incorporate local linearisation methods similar to those which have previously been
employed in the deterministic filtering literature these lead to very effective importance Distributions.
Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of
the analytic structure present in some important classes of state-space models. In a final section we
develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
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
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
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
Sequential Monte Carlo without Likelihoods
粒子濾波不用似然函數(shù)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.
Linux Bible: 介紹包括 Fedora, KNOPPIX, Debian, SUSE, Ubuntu , 及其他 7 種 Linux
* Ideal for users planning the transition to Linux who want to sample different Distributions to see which one best meets their needs
* Includes sections on practical uses for Linux, multimedia apps, instant messaging, BT, and improved security techniques
Boost provides free peer-reviewed portable C++ source libraries.
We emphasize libraries that work well with the C++ Standard Library. Boost libraries are intended to be widely useful, and usable across a broad spectrum of applications. The Boost license encourages both commercial and non-commercial use.
We aim to establish "existing practice" and provide reference implementations so that Boost libraries are suitable for eventual standardization. Ten Boost libraries are already included in the C++ Standards Committee s Library Technical Report (TR1) as a step toward becoming part of a future C++ Standard. More Boost libraries are proposed for the upcoming TR2.
Boost works on almost any modern operating system, including UNIX and Windows variants. Follow the Getting Started Guide to download and install Boost. Popular Linux and Unix Distributions such as Fedora, Debian, and NetBSD include pre-built Boost packages. Boost may also already be available on your organization s internal web server.
Linux was first released into an unsuspecting world in the summer of 1991. Initially
the spare-time hobby of a Finnish computer scientist by the name of Linus Torvalds,
Linux was at first accessible only in software source code form to those with enough
expertise to build and install it. Early enthusiasts (most also developers themselves by
necessity) exploited the growth of the Internet in the early 1990s as a means to build
online communities and drive development forward. These communities helped to
build the first Linux software Distributions, containing all the software components
needed to install and use a Linux system without requiring users to be technical experts.
