This is machine translation
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.
Speed Up Statistical Computations
Statistics and Machine Learning Toolbox™ allows you to use parallel computing to speed up certain statistical computations. In parallel computing, a single MATLAB® client session distributes code segments to multiple workers for independent processing, and then combines these individual results to complete the computation.
Use parallel computing to speed up resampling techniques such as bootstrap and jackknife, boosting and bagging of decision trees, cross-validation, clustering algorithms, and more. For a complete list of Statistics and Machine Learning Toolbox functions that support parallel computing, see Quick Start Parallel Computing for Statistics and Machine Learning Toolbox.
You must have a Parallel Computing Toolbox™ license to use the parallel computing functionality.
Topics
Quick Start Parallel Computing for Statistics and Machine Learning Toolbox
Get started with parallel statistical computing.
Concepts of Parallel Computing in Statistics and Machine Learning Toolbox
Overview of the ideas in parallel statistical computations.
When to Run Statistical Functions in Parallel
Deciding when to call functions in parallel
Parallel computing using parfor with statistics
functions.
Implement Jackknife Using Parallel Computing
Speed up the jackknife using parallel computing.
Implement Cross-Validation Using Parallel Computing
Speed up cross-validation using parallel computing.
Implement Bootstrap Using Parallel Computing
Speed up the bootstrap using parallel computing.
Reproducibility in Parallel Statistical Computations
How to obtain identical results from repeated parallel computations.
