StudentT lam parameter

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

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According to the documentation, nu is the degrees of freedom. Lam is the scale parameter. What is the interpretation for scale parameter?

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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

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