Recent content by dspiegel

Well there're a bit more to it. The formula you quoted is just for the prior. The derivation is thus. p(w|x,t,\alpha,\beta) = (likelihood * prior) / marginal likelihood p(w|x,t,\alpha,\beta) \propto p(t|x,w,\beta) * p(w|\alpha) \{\alpha,\beta\} are hyperparameters.

Thanks for your reply. Although I am quite sure that's not the case in this particular instance, in general, I know non-variable parameters may be written after a semicolon. I believe the case to be that it reads as, "the value of t_n evaluated for y(x_n,\textbf{w})" as described on...

Hey all. Looking at "Pattern Recognition and Machine Learning" (Bishop, 2006) p28-31, the author appears to be using what would ordinarily be a delimiter for a conditional probability inside a linear function. See the first variable in normpdf as below. This is in the context of defining a...