def __init__(self, mean=0, sigma=1, name='', model=None):
super().__init__(name, model)
self.Var('v1', Normal.dist(mu=mean, sigma=sigma))
Normal('v2', mu=mean, sigma=sigma)
Normal('v3', mu=mean, sigma=HalfCauchy('sd', beta=10, testval=1.))
Deterministic('v3_sq', self.v3 ** 2)
Potential('p1', tt.constant(1))
def __init__(self, mean=0, sigma=1, name="", model=None):
super().__init__(name, model)
self.Var("v1", Normal.dist(mu=mean, sigma=sigma))
Normal("v2", mu=mean, sigma=sigma)
Normal("v3", mu=mean, sigma=HalfCauchy("sd", beta=10, testval=1.0))
Deterministic("v3_sq", self.v3 ** 2)
Potential("p1", aet.constant(1))
def fit(self, n_steps=30000):
"""
Creates a Bayesian Estimation model for replicate measurements of
treatment(s) vs. control.
Parameters
----------
n_steps : int
The number of steps to run ADVI.
"""
sample_names = set(self.data[self.sample_col].values)
sample_names.remove(self.baseline_name)
with Model() as model:
# Hyperpriors
upper = Exponential('upper', lam=0.05)
nu = Exponential('nu_minus_one', 1 / 29.) + 1
# "fold", which is the estimated fold change.
fold = Uniform('fold',
lower=1E-10,
upper=upper,
shape=len(sample_names))
# Assume that data have heteroskedastic (i.e. variable) error but
# are drawn from the same HalfCauchy distribution.
sigma = HalfCauchy('sigma', beta=1, shape=len(sample_names))
# Model prediction
mu = fold[self.data['indices']]
sig = sigma[self.data['indices']]
# Data likelihood
like = StudentT('like',
nu=nu,
mu=mu,
sd=sig**-2,
observed=self.data[self.output_col])
self.model = model
with model:
params = advi(n=n_steps)
trace = sample_vp(params, draws=2000)
self.trace = trace