In Statistical Rethinking Richard McElrealth asks the question
Which line is likelihood?
y_i \sim Normal( \mu, \sigma) \\ \mu \sim Normal(0,10) \\ \sigma \sim Uniform(0,10)
The answer is the first line, which he states
The first line is the likelihood. The second line is very similar, but is instead the prior for the parameter μ. The third line is the prior for the parameter σ. Likelihoods and priors can look very similar, because a likelihood is effectively a prior for the residuals.
Does anyone know what he means with the last line? That a likelihood is effectively a prior for the residuals? The residuals of what?