Metadata-Version: 2.1
Name: pymc3-quap
Version: 1.0.2
Summary: Quadratic approximation for PyMC3
Home-page: https://github.com/rasmusbergpalm/pymc3-quap
Author: Rasmus Berg Palm
Author-email: rasmusbergpalm@gmail.com
License: UNKNOWN
Description: # pymc3-quap
        
        The quadratic approximation is a very fast method to approximate the posterior with a multivariate normal. 
        
        NOTE: The quadratic approximation only works well if the posterior is uni-modal and roughly symmetrical. 
        
        ### Example
        
        ```python
        import numpy as np
        import pymc3 as pm
        import arviz as az
        from quap import quap
        y = np.array([2642, 3503, 4358]*10)
        
        # Normal with unknown mean and log-variance, with uniform priors 
        with pm.Model() as m: 
          logsigma = pm.Uniform("logsigma", -100, 100)
          mu = pm.Uniform("mu", -10000, 10000) 
          yobs = pm.Normal("y", mu=mu, sigma=pm.math.exp(logsigma), observed=y)
          idata, posterior = quap([mu, logsigma])
        
        az.plot_posterior(idata)
        ```
        
        ![Approximate posterior](posterior.png)
        
        `idata` is an `arviz.InferenceData` with samples from the approximate posterior for compatibility with the Arviz ecosystem.
         
        `posterior` is the exact approximate posterior `scipy.stats.multivariate_normal`
        
        ![True and quadratic approximation of posterior](quap.png)   
        
        True posterior and quadratic approximation for the example above.
        
        ### Install
        
        `pip install pymc3-quap`
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
