def get_log_likelihood(self):
s2 = numpy.power(self.s_y,2)
#L = ( 1.0/numpy.sqrt(2*numpy.pi*s2) ) ** self.nobs * numpy.exp( -self.ssr/(s2*2.0) )
# logging.debug(( 1.0/numpy.sqrt(2*numpy.pi*s2) ) ** self.nobs)
# logging.debug(numpy.exp( -self.ssr/(s2*2.0) ))
# return self.nobs*( 1.0/numpy.sqrt(2*numpy.pi*s2))-(self.ssr/(s2*2.0))
return (-numpy.log(2*numpy.pi)*self.nobs/2)-\
(numpy.log(s2)*self.nobs/2)-(self.ssr/(s2*2.0))
def get_BIC(self):
"""
Bayesian Information criterion for comparing between models
"""
return self.nparams*numpy.log(self.nobs)-2*self.get_log_likelihood()
