In a fully-Bayesian Functional Principal Components Analysis (FPCA) the principal components are treated as unknown infinite-dimensional parameters. By projecting the functional principal components on a rich orthonormal spline basis, we show that orthonormality of the principal components is equivalent with the orthonormality of the spline coefficients. A penalty on the integral of the second derivative of the functional principal components can be induced on the spline coefficients, where each function has its own smoothing parameter. Finally, each smoothing parameter is treated as an inverse variance component in the associated mixed effects model. In this paper we provide sufficient conditions to ensure that the posterior distribution is proper. This condition is expressed in terms of the eigenvalues of the smoothing penalty design matrix, which provides a practical and simple choice for the prior on the smoothing parameters.

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