def ln_prob(self, val):
"""Returns the log prior probability of val.
Parameters
----------
val: Union[float, int, array_like]
Returns
-------
Union[float, array_like]: Prior probability of val
"""
_ln_prob = xlogy(self.alpha - 1, val - self.minimum) + xlogy(self.beta - 1, self.maximum - val)\
- betaln(self.alpha, self.beta) - xlogy(self.alpha + self.beta - 1, self.maximum - self.minimum)
# deal with the fact that if alpha or beta are < 1 you get infinities at 0 and 1
if isinstance(val, np.ndarray):
_ln_prob_sub = -np.inf * np.ones(len(val))
idx = np.isfinite(_ln_prob) & (val >= self.minimum) & (
val <= self.maximum)
_ln_prob_sub[idx] = _ln_prob[idx]
return _ln_prob_sub
else:
if np.isfinite(
_ln_prob) and val >= self.minimum and val <= self.maximum:
return _ln_prob
return -np.inf
def ln_prob(self, val):
"""Return the log prior probability of val
Parameters
==========
val: Union[float, int, array_like]
Returns
=======
float: log probability of val
"""
return xlogy(1, (val >= self.minimum) & (val <= self.maximum)) - xlogy(1, self.maximum - self.minimum)
def ln_prob(self, val):
"""Returns the log prior probability of val.
Parameters
==========
val: Union[float, int, array_like]
Returns
=======
Union[float, array_like]: Prior probability of val
"""
if isinstance(val, (float, int)):
if val < self.minimum:
_ln_prob = -np.inf
else:
_ln_prob = xlogy(self.k - 1, val) - val / self.theta - xlogy(self.k, self.theta) - gammaln(self.k)
else:
_ln_prob = -np.inf * np.ones(val.size)
idx = (val >= self.minimum)
_ln_prob[idx] = xlogy(self.k - 1, val[idx]) - val[idx] / self.theta\
- xlogy(self.k, self.theta) - gammaln(self.k)
return _ln_prob