RobustGibbs

Name	RobustGibbs JSON
Version	0.0.7 JSON
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home_page
Summary	Package for Gibbs Sampling with Robust Statistics.
upload_time	2023-09-06 14:00:09
maintainer
docs_url	None
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requires_python	>=3.7
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requirements	No requirements were recorded.
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            # RobustGibbs Package

`Robust_Gibbs` is a package that allows users to sample from the parameters posteriors when only some robust statistics of the data are available. The paper that describe all the theory of the methods can be found on arXiV (https://arxiv.org/abs/2307.14973). 

## Main functions

We propose here three mains functions named `Gibbs_med_MAD`, `Gibbs_med_IQR` and `Gibbs_Quantile` to cover the case when we observe the pairs (median, MAD) or (median, IQR) or a sequence of quantiles. 


## Install

Install via clone the repository and install via pip

```shell
git clone https://github.com/???
pip install .
```

## Use

Here, we sample from the posterior of parameters of a normal distribution using the couple of conjuguate couple Normal-InverseGamma. 

```python
# CODE
```

## Available distributions/likelihoods
* Normal distribution (`distribution="normal"`)
* Cauchy distribution (`distribution="cauchy"`)
* Weibull distribution (`distribution="weibull"`)
* Translated distribution (`distribution="translated_weibull"`)

## Available location priors
* Normal (`par_loc="normal"`)
* Cauchy (`par_loc="cauchy"`)
* Gamma (`par_loc="gamma"`)

## Available scale priors
* Gamma (`par_scale="gamma"`)
* Jeffreys (`par_scale="jeffreys"`)

## Available shape prior
* Gamma (`par_shape="gamma"`)



            

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