Metadata-Version: 2.1
Name: bqme
Version: 0.0.1
Summary: Bayesian Quanile Matching Estimation
Home-page: https://github.com/RSNirwan/BQME
Author: Rajbir Singh Nirwan
Author-email: rajbir.nirwan@gmail.com
License: UNKNOWN
Description: # Bayesian Quantile Matching Estimation using Order Statistics
        
        
        BQME is a package that allows users to fit a distribution to observed quantile data. The package uses Order Statistics as the noise model, which is more robust than e.g. Gaussian noise model (mean squared error). The paper describing the theory can be found on arxiv: [https://arxiv.org/abs/2008.06423](https://arxiv.org/abs/2008.06423). The notebooks for the experiments in the paper are moved to [https://github.com/RSNirwan/BQME_experiments](https://github.com/RSNirwan/BQME_experiments).
        
        
        ## Install
        
        Clone the repository and install via pip
        
        ```shell
        git clone https://github.com/RSNirwan/bqme
        cd bqme
        pip install .
        ```
        
        Install with dev dependencies 
        
        ```shell
        pip install -e .[dev]
        ```
        if using ZSH, do the following  `pip install -e ".[dev]"`
        
        
        ## Usage
        
        To fit a Normal distribution to observed quantile data, we do
        
        ```python
        from bqme.distributions import Normal, Gamma
        from bqme.models import Normal_qm
        
        N, q, X = 100, [0.25, 0.5, 0.75], [-0.1, 0.3, 0.8]
        
        # define prior
        mu = Normal(0, 1, name='mu')
        sigma = Gamma(0, 1, name='sigma)
        
        # define likelihood
        model = Normal_qm(mu, sigma)
        
        # fit model
        fit = model.sampling(N, q, X)
        ```
        
        ## Todos
        
        - [ ] make package available on PyPI
        - [ ] use sphinx as documentation tool
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
