ums_flat_prior#

agabpylib.posteriors.meanvarnormal.ums_flat_prior(n, xbar, V, mu, sigma)#

Calculate the joint posterior distribution for the unknown mean \(\mu\) and unknown standard deviation \(\sigma\) for a flat prior in both parameters.

The posterior is calculated over the input grid in \((\mu, \sigma)\) and for the data characterized by the number of samples i:math:n, the mean \(\bar{x}\), and the data variance \(V\).

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) – 2D-array of \(\mu\) values (obtained through numpy.meshgrid for example)

  • sigma (float array) – 2D-array of \(\sigma\) values

Returns:

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

Return type:

float array