def MakeGaussianPmf(mu, sigma, num_sigmas, n=<NUM_LIT>):
pmf = Pmf()<EOL>low = mu - num_sigmas * sigma<EOL>high = mu + num_sigmas * sigma<EOL>for x in numpy.linspace(low, high, n):<EOL><INDENT>p = EvalGaussianPdf(x, mu, sigma)<EOL>pmf.Set(x, p)<EOL><DEDENT>pmf.Normalize()<EOL>return pmf<EOL>
Makes a PMF discrete approx to a Gaussian distribution.

    mu: float mean
    sigma: float standard deviation
    num_sigmas: how many sigmas to extend in each direction
    n: number of values in the Pmf

    returns: normalized Pmf