def norm_lls(y, params):
 (mu, sigma2) = params.T
 lls = ((-0.5) * ((np.log((2 * np.pi)) + np.log(sigma2)) + (((y - mu) ** 2) / sigma2)))
 return lls

def norm_lls ( y , params ) : ( mu , sigma2 ) = params . T lls = ( ( - 0.5 ) * ( ( np . log ( ( 2 * np . pi ) ) + np . log ( sigma2 ) ) + ( ( ( y - mu ) ** 2 ) / sigma2 ) ) ) return lls
normal loglikelihood given observations and mean mu and variance sigma2 parameters y : array .