# Exploratory Bayesian Analysis[¶](https://www.dataiku.com/product/plugins/monte-carlo/)

## This plugin provides a Jupyter notebook template to perform Bayesian analysis with MCMC simulations[¶](https://www.dataiku.com/product/plugins/monte-carlo/)

This plugin provides a notebook template to perform a simple Bayesian inference analysis on one numerical column of a dataset (observations).    The main goal is to perform inference on the variables that parametrize the distribution from which the observations were theoretically generated.

## Plugin Information[¶](https://www.dataiku.com/product/plugins/monte-carlo/)

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| Version | 0.0.4 |

| Author | Dataiku (Harizo Rajaona) |

| Released | 2018-08-01 |

| Last updated | 2018-08-01 |

## How To Use[¶](https://www.dataiku.com/product/plugins/monte-carlo/)

The notebook template is available by selecting a dataset, go to “Lab”, and in the notebooks section, choosing the appropriate pre-defined template.

The notebook content is mostly self-explanatory, but it should not be executed in one single run of all the cells, it was designed so that the user is guided to build his own probabilistic model that will vary depending on the use-case and the data itself.

### Install In DSS[¶](https://www.dataiku.com/product/plugins/monte-carlo/)

To install the plugin, open the  Apps menu, click Plugins and search for Exploratory Bayesian Analysis.

Alternatively, you can download a zipped version here.

### Get the Dataiku Data Sheet[¶](https://www.dataiku.com/product/plugins/monte-carlo/)

Learn everything you ever wanted to know about Dataiku (but were afraid to ask), including detailed specifications on features and integrations.
