Parallel or distributed computation of statistical
functions

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.

**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.

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