marginal_mean_ums_flat_prior

agabpylib.posteriors.meanvarnormal.marginal_mean_ums_flat_prior(n, xbar, V, mu)

Calculate the marginal posterior distribution of \(\mu\) for the case of unknown mean and unknown standard deviation.

Parameters:

• n (int) – The number of data points \(x_i\)

• xbar (float) – The mean of the data points \(x_i\)

• V (float) – The data variance \(\sum(x_i-\bar{x})^2/n\)

• mu (float array) – 1D-array of \(\mu\) values

Returns:

lnP – The value of ln(posterior) at each grid point.

Return type:

float array