Abstract: Prior sensitivity analysis and cross-validation are important tools in
Bayesian statistics. However, due to the computational complexity of existing
methods, these techniques are rarely used. In this talk, we show how it is possible
to use sequential Monte Carlo to create an efficient and automated algorithm to
perform these tasks. We apply the algorithm to the creation of regularization path
plots and to check the sensitivity of the tuning parameter in g-Prior model selection.
We then demonstrate the algorithm applied to cross-validation and use it to select
the shrinkage parameter in Bayesian penalized regression.