def model(data, params):
# XXX: this model currenty NaNs
# initialize data
G = data["G"]
N = data["N"]
r = data["r"]
n = data["n"]
# model block
with pyro.plate('a_', G, dim=-2):
mu = pyro.sample('mu', dist.Uniform(0., 1.))
a_plus_b = pyro.sample('a_plus_b', dist.Pareto(0.1, 1.5))
a = mu * a_plus_b
b = (1 - mu) * a_plus_b
with pyro.plate('data', N, dim=-1):
p = pyro.sample('p', dist.Beta(a, b))
r = pyro.sample('r', dist.Binomial(n, p), obs=r)
def get_collisionMinGapFactor(self):
if self.is_using:
return min(2, max(1, distrs.Pareto(1, 2)().item())) - 1
return 0
def get_minGap(self):
if self.is_using:
return min(2, max(1, distrs.Pareto(1, 1)().item())) - 1
return 0
def Pareto(_name, scale, alpha):
return {'x': pyro.sample(_name, dist.Pareto(scale, alpha))}