forestci/calibration.py
def gbayes(x0, g_est, sigma):
    """
    Estimate Bayes posterior with Gaussian noise [Efron2014]_.

    Parameters
    ----------
    x0: ndarray
        an observation
    g_est: float
        a prior density, as returned by gfit
    sigma: int
        noise estimate

    Returns
    -------
    An array of the posterior estimate E[mu | x0]
    """

    Kx = norm().pdf((g_est[0] - x0) / sigma)
    post = Kx * g_est[1]
    post /= sum(post)
    return sum(post * g_est[0])
def gbayes(x0, g_est, sigma):
    """
    Estimate Bayes posterior with Gaussian noise [Efron2014]_.

    Parameters
    ----------
    x0: ndarray
        an observation
    g_est: float
        a prior density, as returned by gfit
    sigma: int
        noise estimate

    Returns
    -------
    An array of the posterior estimate E[mu | x0]
    """

    Kx = norm().pdf((g_est[0] - x0) / sigma)
    post = Kx * g_est[1]
    post /= sum(post)
    return sum(post * g_est[0])
def gbayes ( x0 , g_est , sigma ) : Kx = norm ( ) . pdf ( ( g_est [ 0 ] - x0 ) / sigma ) post = Kx * g_est [ 1 ] post /= sum ( post ) return sum ( post * g_est [ 0 ] )
Estimate Bayes posterior with Gaussian noise [Efron2014]_.

    Parameters
    ----------
    x0: ndarray
        an observation
    g_est: float
        a prior density, as returned by gfit
    sigma: int
        noise estimate

    Returns
    -------
    An array of the posterior estimate E[mu | x0]
Estimate Bayes posterior with Gaussian noise [ Efron2014 ] _ .