def log_gaussian(x, mean, sigma):
    '\n    Computes the log-probability of X=x for a Gaussian of mean=mean and sigma=sigma\n    Parameters\n    ----------\n    x\n    mean\n    sigma\n\n    Returns\n    -------\n\n    '
    log_pdf = ((- ((x - mean) ** 2)) / (2 * (sigma ** 2)))
    log_pdf = (log_pdf - np.log((np.sqrt((2 * np.pi)) * sigma)))
    return log_pdf
Computes the log-probability of X=x for a Gaussian of mean=mean and sigma=sigma
Parameters
----------
x
mean
sigma

Returns
-------
def log_gaussian(x, mean, sigma):
    '\n    Computes the log-probability of X=x for a Gaussian of mean=mean and sigma=sigma\n    Parameters\n    ----------\n    x\n    mean\n    sigma\n\n    Returns\n    -------\n\n    '
    log_pdf = ((- ((x - mean) ** 2)) / (2 * (sigma ** 2)))
    log_pdf = (log_pdf - np.log((np.sqrt((2 * np.pi)) * sigma)))
    return log_pdf