If X is a r.v. and beta a fixed parameter (say, an MLE estimate), we know that: Var(beta*X) = beta^2 * Var(X).
I wonder whether this holds in a bayesian framework, where beta is itself a r.v.
Thank you
If X is a r.v. and beta a fixed parameter (say, an MLE estimate), we know that: Var(beta*X) = beta^2 * Var(X).
I wonder whether this holds in a bayesian framework, where beta is itself a r.v.
Thank you
If you assume the random variables are independent I believe there is an explicit formula.
V(XY) = (E[X])^2*V[Y] + (E[Y])^2*V[X] + V[X]V[Y]
Thank you very much!