Clik here to view.
Intel® Summary Statistics Library: Several Estimates at One Stroke
Today it was necessary for me to compute statistical estimates for a dataset. The observations are weighted, and only several components of the random vector had to be analyzed. How often do we solve...
View ArticleClik here to view.
Intel® Summary Statistics Library: how to process data in chunks?
In my previous post I considered computation of statistical estimates for in-memory datasets using tools available in Intel® Summary Statistics Library. New days bring new problems, and today I need...
View ArticleClik here to view.
Intel® Summary Statistics Library: how to detect outliers in datasets?
Earlier I computed various statistical estimates like mean or variance-covariance matrix using Intel® Summary Statistics Library. In those cases I knew for sure that my datasets did not contain “bad”...
View ArticleClik here to view.
Intel® Summary Statistics Library: why not to use multi-core advantages?
In my previous posts I described some features and usage model of Intel® Statistics Library. However, there are many available statistical packages that provide good similar functionality. Does Intel®...
View ArticleClik here to view.
Intel® Summary Statistics Library: how fast is the algorithm for detection of...
In one of my previous posts I described the scheme for detection of outliers in datasets which is important component of the Intel® Summary Statistics Library. We included optimized version of this...
View ArticleClik here to view.
Intel® Summary Statistics Library: how to use the robust methods?
Intel® Summary Statistics Library provides several opportunities for processing the datasets “contaminated” with outliers. Earlier I demonstrated how to detect “suspicious” observations in the...
View ArticleClik here to view.
Intel® Summary Statistics Library: how to deal with missing observations?
Real life datasets can have missing values. Sociological surveys and measurement of complex biological systems are two examples where the researcher can arrive at the point in which he should do...
View ArticleUsing Intel® MKL with R
OverviewR is a programming language for statistical computing. The open source package also provides an environment for creating and running R programs. This guide will show how to use the BLAS and...
View ArticleClik here to view.
Extending R with Intel MKL
IntroductionThe Using Intel MKL with R article discusses building R with the Intel® Math Kernel Library (Intel® MKL) BLAS and LAPACK to improve the performance of those parts of R that rely on matrix...
View ArticleBuild R-3.0.1 with Intel® C++ Compiler and Intel® MKL on Linux*
1. Download R at CRAN(Comprehensive R Archive Network),CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Please use...
View ArticleClik here to view.
Intel® Summary Statistics Library: Several Estimates at One Stroke
Today it was necessary for me to compute statistical estimates for a dataset. The observations are weighted, and only several components of the random vector had to be analyzed. How often do we solve...
View ArticleClik here to view.
Intel® Summary Statistics Library: how to process data in chunks?
In my previous post I considered computation of statistical estimates for in-memory datasets using tools available in Intel® Summary Statistics Library. New days bring new problems, and today I need to...
View ArticleClik here to view.
Intel® Summary Statistics Library: how to detect outliers in datasets?
Earlier I computed various statistical estimates like mean or variance-covariance matrix using Intel® Summary Statistics Library. In those cases I knew for sure that my datasets did not contain “bad”...
View ArticleClik here to view.
Intel® Summary Statistics Library: why not to use multi-core advantages?
In my previous posts I described some features and usage model of Intel® Statistics Library. However, there are many available statistical packages that provide good similar functionality. Does Intel®...
View ArticleClik here to view.
Intel® Summary Statistics Library: how fast is the algorithm for detection of...
In one of my previous posts I described the scheme for detection of outliers in datasets which is important component of the Intel® Summary Statistics Library. We included optimized version of this...
View ArticleClik here to view.
Intel® Summary Statistics Library: how to use the robust methods?
Intel® Summary Statistics Library provides several opportunities for processing the datasets “contaminated” with outliers. Earlier I demonstrated how to detect “suspicious” observations in the...
View ArticleClik here to view.
Intel® Summary Statistics Library: how to deal with missing observations?
Real life datasets can have missing values. Sociological surveys and measurement of complex biological systems are two examples where the researcher can arrive at the point in which he should do...
View ArticleUsing Intel® MKL with R
OverviewR is a programming language for statistical computing. The open source package also provides an environment for creating and running R programs. This guide will show how to use the BLAS and...
View ArticleExtending R with Intel MKL
IntroductionThe Using Intel MKL with R article discusses building R with the Intel® Math Kernel Library (Intel® MKL) BLAS and LAPACK to improve the performance of those parts of R that rely on matrix...
View ArticleBuild R-3.0.1 with Intel® C++ Compiler and Intel® MKL on Linux*
1. Download R at CRAN(Comprehensive R Archive Network),CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Please use...
View Article
