Penalized complexity priors for degrees of freedom in Bayesian P-splines
Bayesian P-splines assume an intrinsic Gaussian Markov random field prior on the spline coefficients, conditional on a precision hyper-parameter $ au$. Prior elicitation of $
Bayesian P-splines assume an intrinsic Gaussian Markov random field prior on the spline coefficients, conditional on a precision hyper-parameter $ au$. Prior elicitation of $