One way to make decisions under uncertainty is to select an optimal option from a possible
range of options, by maximizing the expected utilities derived from a probability model …

Binary classification is a well known problem in statistics. Besides classical methods, several
techniques such as the naive credal classifier (for categorical data) and imprecise logistic …

We propose a cautious Bayesian variable selection routine by investigating the sensitivity of
a hierarchical model, where the regression coefficients are specified by spike and slab …

Modern scientific experiments often rely on different statistical tools, regularisation being one
of them. Regularisation methods are usually used to avoid overfitting but we may also want …

Decision makers are often faced with making single hard decisions, without having any
knowledge of the amount of uncertainties contained in them, and taking the risk of making …

Often, in dynamical systems, such as farmers' crop choices, the dynamics are driven by
external non-stationary factors, such as rainfall and agricultural input and output prices …

Bayesian variable selection is one of the popular topics in modern day statistics. It is an
important tool for high dimensional statistics, where the number of model parameters is …