Metadata-Version: 2.1
Name: BayesBoom
Version: 0.0.12
Summary: Tools for Bayesian modeling.
Home-page: https://github.com/steve-the-bayesian/BOOM
Author: Steven L. Scott
Author-email: steve.the.bayesian@gmail.com
License: UNKNOWN
Description: Boom stands for 'Bayesian object oriented modeling'.
            It is also the sound your computer makes when it crashes.
        
            The main part of the Boom library is formulated in terms of abstractions
            for Model, Data, Params, and PosteriorSampler.  A Model is primarily an
            environment where parameters can be learned from data.  The primary
            learning method is Markov chain Monte Carlo, with custom samplers defined
            for specific models.
        
            The archetypal Boom program looks something like this:
        
            import BayesBoom as Boom
        
            some_data = 3 * np.random.randn(100) + 7
            model = Boom.GaussianModel()
            model.set_data(some_data)
            precision_prior = Boom.GammaModel(0.5, 1.5)
            mean_prior = Boom.GaussianModel(0, 10**2)
            poseterior_sampler = Boom.GaussianSemiconjugateSampler(
                model, mean_prior, precision_prior)
            model.set_method(poseterior_sampler)
            niter = 100
            mean_draws = np.zeros(niter)
            sd_draws = np.zeros(niter)
            for i in range(100):
                model.sample_posterior()
                mean_draws[i] = model.mu()
                sd_draws[i] = model.sigma()
        
            
Platform: UNKNOWN
Description-Content-Type: text/plain
