We propose a Bayesian elastic net that uses empirical likelihood and develop an efficient tuning of Hamiltonian Monte Carlo for posterior sampling. The proposed model relaxes the assumptions on identity error distribution, performs well when variables are highly correlated, enables more straightforward inference by providing distributions regression coefficients. method implemented in overcomes...

The likelihood approach used in this paper leads to quadratic discriminant functions. Classification into one of two known multivariate normal populations with a and unknown covariance matrix are separately considered, where the cases depend on sample size an squared Mahalanobis distance. Their exact distributions complicated obtain. Therefore, moments for based functions established express ba...

Marginal MAP is known to be a difficult task for graphical models, particularly because the evaluation of each MAP assignment involves a conditional likelihood computation. In order to minimize the number of likelihood evaluations, we focus in this paper on best-first search strategies for exploring the space of partial MAP assignments. We analyze the potential relative benefits of several best...