Home
Roger Chen's Library tagged → View Popular
Singular Value Decomposition Tutorial
SVD is extraordinarily useful and has many applications such as data analysis, signal processing, pattern recognition, image compression, weather prediction, and Latent Semantic Analysis or LSA (also referred to as Latent Semantic Indexing or LSI).
Google Tech Talk Review: Statistical Aspects of Data Mining | A Beautiful WWW
This is a talk series being given at Google by David Mease based on a Master’s level stats course he is teaching this summer at Stanford. Its easy listening if you already have some data mining or stats background.
Statistical Data Mining Tutorials by Andrew Moore
Andrew Moore
Data Miners Blog: Data Mining and Statistics
-
The way I think about it, data mining is the process of using data to figure stuff out.
-
There is, however, a cultural difference between people who call themselves statisticians and people who call themselves data miners. This difference has its origins in different expectations about data size.
MultiVariate Pattern Analysis (MVPA) in Python
PyMVPA is a Python module intended to ease pattern classification analyses of large datasets. In the neuroimaging contexts such analysis techniques are also known as decoding or MVPA analysis. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run.
Statistical Reference Datasets (StRD)
The purpose of this project is to improve the accuracy of statistical software
by providing reference datasets with certified computational results that enable
the objective evaluation of statistical software.
MCMC(Markov chain Monte Carlo) tutorial
Markov chain Monte Carlo is a general computing technique that has been widely used in physics,
chemistry, biology, statistics, and computer science. It simulates a Markov chain whose invariant
states follow a given (target) probability in a very high (say millions) dimensional state space.
Essentially, it generates fair samples from a probability which are used for many purposes.
Selected Tags
Related Tags
Sponsored Links
Top Contributors
Groups interested in statistics
-
2008 Stimulus Plan
Items: 35 | Visits: 102
Created by: Joanna Yu
-
Research
Research sites sites givin...
Items: 67 | Visits: 92
Created by: dreaming spires
Diigo is about better ways to research, share and collaborate on information. Learn more »
Join Diigo