Statistics, Bayesian Statistics, Optimization And Ml, Machine Learning, Programming.
some monotonic, one-to-one, onto function. Then a uniform prior over u is equivalent to choosing a prior over θ. So, when does it make sense to use a uniform prior if the choice is sensitive to parameterization? Let’s consider how the likelihood is affected by changes in the observed value y^⊤y.