nessai-models


Namenessai-models JSON
Version 0.5.0 PyPI version JSON
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home_pageNone
SummaryModels for nessai
upload_time2024-04-02 11:22:05
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docs_urlNone
authorNone
requires_python>=3.8
licenseMIT
keywords nested sampling normalizing flows machine learning nessai
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            [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7105559.svg)](https://doi.org/10.5281/zenodo.7105559)
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# nessai-models

Models for use with the nested sampling package [`nessai`](https://github.com/mj-will/nessai).

## Included models

* n-dimensional unit Gaussian
* n-dimensional HalfGaussian
* n-dimensional Rosenbrock
* n-dimensional mixture of Gaussians
* n-dimensional slab plus spike model
* Gaussian mixture using data to based on [this example](https://github.com/johnveitch/cpnest/blob/master/examples/gaussianmixture.py) from `cpnest`
* n-dimensional Egg Box based on the version in [Feroz et al. 2008](https://arxiv.org/abs/0809.3437)
* n-dimensional Pyramid-like model
* n-dimensional Brewer likelihood (Skilling's "Staistical Model") from [Brewer et al.](https://arxiv.org/abs/0912.2380)
* Linear signal plus Gaussian noise model (`LinearSignal`)
* Sinusoidal signal plus Gaussian noise model (`SinusoidalSignal`)
* Mixture of 1-dimensional distributions (`MixtureOfDistributions`)

## Requirements

`nessai_models` requires:

* `numpy`
* `scipy`
* `nessai>=0.6.0`

## Installation

> We recommend following the [installation instructions for `nessai`](https://github.com/mj-will/nessai#installation) and then installing `nessai_models` since it shares all of its dependencies with `nessai`.

`nessai_models` can be install from PyPI using

```console
pip install nessai-models
```

## Example usage

Below is an example of using `nessai_models` so configure a 4-dimensional Gaussian and then sample it using `nessai`.

```python
from nessai import FlowSampler
from nessai_models import Gaussian

model = Gaussian(4)
fs = FlowSampler(model, output='example/')
fs.run()
```

## Citing

If you use `nessai_models` in your work please cite the [Zenodo DOI](https://doi.org/10.5281/zenodo.7105559)

            

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