# Path: distributions/dbg/special.py

# 
# Path: distributions/dbg/random.py
# def sample_chi2(nu):
#     return chi2.rvs(nu)
# 
# def sample_normal(mu, sigmasq):
#     return norm.rvs(mu, sigmasq)


from distributions.dbg.special import sqrt, log, pi, gammaln
from distributions.dbg.random import sample_chi2, sample_normal
from distributions.mixins import SharedMixin, GroupIoMixin, SharedIoMixin
            'count': self.count,
            'mean': self.mean,
            'count_times_variance': self.count_times_variance,
        }

    def protobuf_load(self, message):
        self.count = int(message.count)
        self.mean = float(message.mean)
        self.count_times_variance = float(message.count_times_variance)

    def protobuf_dump(self, message):
        message.count = self.count
        message.mean = self.mean
        message.count_times_variance = self.count_times_variance


class Sampler(object):
    def init(self, shared, group=None):
        """
        Draw samples from the marginal posteriors of mu and sigmasq

        \cite{murphy2007conjugate}, Eqs. 156 & 167
        """
        post = shared if group is None else shared.plus_group(group)
        # Sample from the inverse-chi^2 using the transform from the chi^2
        sigmasq_star = post.nu * post.sigmasq / sample_chi2(post.nu)
        self.sigma = sqrt(sigmasq_star)
        self.mu = sample_normal(post.mu, sqrt(sigmasq_star / post.kappa))

    def eval(self, shared):

# Path: distributions/dbg/special.py

# 
# Path: distributions/dbg/random.py
# def sample_chi2(nu):
#     return chi2.rvs(nu)
# 
# def sample_normal(mu, sigmasq):
#     return norm.rvs(mu, sigmasq)


# Path: distributions/dbg/models/nich.py
from distributions.dbg.special import sqrt, log, pi, gammaln
from distributions.dbg.random import sample_chi2, sample_normal
from distributions.mixins import SharedMixin, GroupIoMixin, SharedIoMixin

            'count': self.count,
            'mean': self.mean,
            'count_times_variance': self.count_times_variance,
        }

    def protobuf_load(self, message):
        self.count = int(message.count)
        self.mean = float(message.mean)
        self.count_times_variance = float(message.count_times_variance)

    def protobuf_dump(self, message):
        message.count = self.count
        message.mean = self.mean
        message.count_times_variance = self.count_times_variance


class Sampler(object):
    def init(self, shared, group=None):
        """
        Draw samples from the marginal posteriors of mu and sigmasq

        \cite{murphy2007conjugate}, Eqs. 156 & 167
        """
        post = shared if group is None else shared.plus_group(group)
        # Sample from the inverse-chi^2 using the transform from the chi^2
        sigmasq_star = post.nu * post.sigmasq / sample_chi2(post.nu)
        self.sigma = sqrt(sigmasq_star)
        self.mu = sample_normal(post.mu, sqrt(sigmasq_star / post.kappa))

    def eval(self, shared):

        return sample_normal(self.mu, self.sigma)