import scipy.stats
[docs]
def normal_Prior(center,width,random):
"""Generate a normal prior distribution for a given parameter,
Args:
center (float): center value of this gaussian distribution.
width (float): width of this gaussian distribution, this is the 1-sigma width.
random (float): random number generated to do inference, this is follow the
definition of baysian workflow of UltraNest, here default to be cube[i]
Returns:
ppf (float): ppf of this distribution function
"""
return scipy.stats.norm(center, width).ppf(random)
[docs]
def flat_prior(low, up,random):
"""Generate a flat prior distribution for a given parameter,
Args:
low (float): lower bound of this flat distribution.
up (float): upper bound of this flat distribution.
random (float): random number generated to do inference, this is follow the
definition of baysian workflow of UltraNest, here default to be cube[i]
Returns:
ppf (float): ppf of this distribution function
"""
return low + (up - low) * random