marginal_sigma_ums_flat_prior#
- agabpylib.posteriors.meanvarnormal.marginal_sigma_ums_flat_prior(n, V, sigma)#
Calculate the marginal posterior distribution of \(\sigma\) for the case of unknown mean and unknown standard deviation.
- Parameters:
n (int) – The number of data points \(x_i\)
V (float) – The data variance \(\sum(x_i-\bar{x})^2/n\)
sigma (float array) – 1D-array of \(\sigma\) values
- Returns:
lnP – The value of ln(posterior) at each grid point.
- Return type:
float array