def logp(self, value):
r"""
Calculate log-probability of ZeroInflatedNegativeBinomial distribution at specified value.
Parameters
----------
value: numeric
Value(s) for which log-probability is calculated. If the log probabilities for multiple
values are desired the values must be provided in a numpy array or theano tensor
Returns
-------
TensorVariable
"""
alpha = self.alpha
mu = self.mu
psi = self.psi
logp_other = tt.log(psi) + self.nb.logp(value)
logp_0 = logaddexp(
tt.log1p(-psi),
tt.log(psi) + alpha * (tt.log(alpha) - tt.log(alpha + mu)))
logp_val = tt.switch(tt.gt(value, 0), logp_other, logp_0)
return bound(logp_val, 0 <= value, 0 <= psi, psi <= 1, mu > 0,
alpha > 0)
def logp(self, value):
r"""
Calculate log-probability of ZeroInflatedBinomial distribution at specified value.
Parameters
----------
value: numeric
Value(s) for which log-probability is calculated. If the log probabilities for multiple
values are desired the values must be provided in a numpy array or theano tensor
Returns
-------
TensorVariable
"""
psi = self.psi
p = self.p
n = self.n
logp_val = tt.switch(
tt.gt(value, 0),
tt.log(psi) + self.bin.logp(value),
logaddexp(tt.log1p(-psi),
tt.log(psi) + n * tt.log1p(-p)),
)
return bound(logp_val, 0 <= value, value <= n, 0 <= psi, psi <= 1,
0 <= p, p <= 1)
def logp(self, value):
psi = self.psi
theta = self.theta
logp_val = tt.switch(tt.gt(value, 0),
tt.log(psi) + self.pois.logp(value),
logaddexp(tt.log1p(-psi),
tt.log(psi) - theta))
return bound(logp_val, 0 <= value, 0 <= psi, psi <= 1, 0 <= theta)
def logp(self, value):
psi = self.psi
p = self.p
n = self.n
logp_val = tt.switch(
tt.gt(value, 0),
tt.log(psi) + self.bin.logp(value),
logaddexp(tt.log1p(-psi),
tt.log(psi) + n * tt.log1p(-p)))
return bound(logp_val, 0 <= value, value <= n, 0 <= psi, psi <= 1,
0 <= p, p <= 1)
def logp(self, value):
alpha = self.alpha
mu = self.mu
psi = self.psi
logp_other = tt.log(psi) + self.nb.logp(value)
logp_0 = logaddexp(
tt.log1p(-psi),
tt.log(psi) + alpha * (tt.log(alpha) - tt.log(alpha + mu)))
logp_val = tt.switch(tt.gt(value, 0), logp_other, logp_0)
return bound(logp_val, 0 <= value, 0 <= psi, psi <= 1, mu > 0,
alpha > 0)
def logp(self, value):
psi = self.psi
theta = self.theta
logp_val = tt.switch(
tt.gt(value, 0),
tt.log(psi) + self.pois.logp(value),
logaddexp(tt.log1p(-psi), tt.log(psi) - theta))
return bound(
logp_val,
0 <= value,
0 <= psi, psi <= 1,
0 <= theta)
def logp(self, value):
psi = self.psi
p = self.p
n = self.n
logp_val = tt.switch(
tt.gt(value, 0),
tt.log(psi) + self.bin.logp(value),
logaddexp(tt.log1p(-psi), tt.log(psi) + n * tt.log1p(-p)))
return bound(
logp_val,
0 <= value, value <= n,
0 <= psi, psi <= 1,
0 <= p, p <= 1)
def logp(self, value):
alpha = self.alpha
mu = self.mu
psi = self.psi
logp_other = tt.log(psi) + self.nb.logp(value)
logp_0 = logaddexp(
tt.log1p(-psi),
tt.log(psi) + alpha * (tt.log(alpha) - tt.log(alpha + mu)))
logp_val = tt.switch(
tt.gt(value, 0),
logp_other,
logp_0)
return bound(
logp_val,
0 <= value,
0 <= psi, psi <= 1,
mu > 0, alpha > 0)
