Metadata-Version: 2.1
Name: bqme
Version: 0.0.3
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
        
        Install latest release via `pip`
        
        ```shell
        pip install bqme
        ```
        
        Latest development version can be installes as follows (@todo make develop branch the default).
        Clone the repository and install via pip
        
        ```shell
        git clone https://github.com/RSNirwan/bqme
        cd bqme
        pip install .
        ```
        
        Install with dev dependencies 
        
        ```shell
        git clone https://github.com/RSNirwan/bqme
        cd bqme
        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 the following. Note that the likelihood is not a Normal distribution, but the order statistics of the observed quantiles assuming the underlying distribution is a Normal.
        
        ```python
        from bqme.distributions import Normal, Gamma
        from bqme.models import NormalQM
        
        N, q, X = 100, [0.25, 0.5, 0.75], [-0.1, 0.3, 0.8]
        
        # define priors
        mu = Normal(0, 1, name='mu')
        sigma = Gamma(1, 1, name='sigma')
        
        # define likelihood
        model = NormalQM(mu, sigma)
        
        # sample the posterior
        samples = model.sampling(N, q, X)  # returns a stan fit object
        
        # extract samples
        mu_samples = samples.extract('mu')['mu']
        sigma_samples = samples.extract('sigma')['sigma']
        ```
        
        We can also look at the generated stan code and optimize the parameters (MAP) instead of sampling the posterior
        
        ```python
        mu = Normal(0, 1, name='mu')
        sigma = Gamma(1, 1, name='sigma')
        model = NormalQM(mu, sigma)
        
        # print generated stan code
        print(model.code)
        
        # optimize
        N, q, X = 100, [0.25, 0.5, 0.75], [-0.1, 0.3, 0.8]
        opt = model.optimizing(N, q, X)
        
        # extract optimized parameters
        mu_opt = opt['mu']
        sigma_opt = opt['sigma']
        ```
        
        ## (so far) Available distributions (prior) and models
        
        distributions/priors (import from `bqme.distributions`): 
        
        * `Normal(mu:float, sigma:float)`
        * `Gamma(alpha:float, beta:float)`
        
        models (import from `bqme.models`):
        
        * `NormalQM(mu:distribution, sigma:distribution)`
        
        Inputs to the models need to be distributions.
        
        ## Todos
        
        - [ ] make package available on PyPI
        - [ ] tag/release on github
        - [ ] add code coverage
        - [ ] testing with nox
        - [ ] use sphinx as documentation tool
        - [ ] add Mixture-model
        - [ ] implement fit.ppf(q), fit.cdf(x), fit.pdf(x), ...
        
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
