In a paper that I was reading Bayesian stats, they are talking about a "tight" prior.
We control the “tightness” of the Minnesota prior by adjusting the values of parameter $b1$. A tight version of the Minnesota prior is de ned by $b1 = 0.2^2$, and a loose version sets $b1 = 0.9^2$.
Is a tight prior the same thing as an "informative prior"?
Is it saying that the variance of a distribution, if we take the example of a normal distribution, is smaller?