What is the interpretation of the lam parameter in StudentT distribution? I am using a bayesian linear regression with StudentT as the prior. In the result, when I get the mean coefficients for all the features, there is one value for lam also. How to interpret this?
It’s the degrees of freedom of the t distribution. When the degrees of freedom are very large, then the distribution is equal to a gaussian. When lam is very small, then the tails carry much more weight
According to the documentation, nu is the degrees of freedom. Lam is the scale parameter. What is the interpretation for scale parameter?
Oops. Sorry for the mix-up. nu are the degrees of freedom and lam is the precision of the gaussian to which the t distribution converges when nu gets really big. The precision is the inverse of the variance of the gaussian. So the bigger the precision, the narrower the distribution is