  I present all the details in calculating the posterior distribution of the
conjugate Normal-Gamma prior in Bayesian Linear Models (BLM), including
correlated observations, prediction, model selection and comments on efficient
numeric implementations. A Python implementation is also presented. These have
been presented and available in many books and texts but, I believe, a general
compact and simple presentation is always welcome and not always simple to
find. Since correlated observations are also included, these results may also
be useful for time series analysis and spacial statistics. Other particular
cases presented include regression, Gaussian processes and Bayesian Dynamic
Models.
