Abstract: The present disclosure concerns a computer-implemented method for selecting an alternative, from a discrete set of alternatives, that is preferable with respect to one or more objectives, comprising: determining a Lagrangian function of the alternatives and one or more parameters; selecting an initial alternative from the set; determining values for the parameters; and selecting, via one or more iterations, an alternative from the set that reduces the Lagrangian subject to the determined parameter values. Further procedures can include determining at least two alternatives, wherein selecting a first alternative results in preference for a second alternative; dividing the set, including the first and second alternatives, into two disjoint subsets; maintaining branches for each subset while eliminating unfeasible branches; and selecting the alternative corresponding to a branch that reaches an optimal value or remains after eliminating all other branches.

Abstract: An exemplary methodology, procedure, system, method and computer-accessible medium can be provided to assign a joint-probability distribution to a set of continuous variables, given a sample of independent joint observations. The exemplary embodiments can transform at least a portion of particular data into transformed data, repeat the transformation until a marginal distribution of at least the portion of the particular data has been transformed to be at least sufficiently near or to reach a predetermined distribution, and estimate at least one likelihood of at least one predetermined result associated with the particular data as a function of the transformed data, whereas the repeated transformation ascends a likelihood function and/or is performed by unitary transformations.

Abstract: An exemplary methodology, procedure, system, method and computer-accessible medium can be provided to assign a joint-probability distribution to a set of continuous variables, given a sample of independent joint observations. The exemplary embodiments can transform at least a portion of particular data into transformed data, repeat the transformation until a marginal distribution of at least the portion of the particular data has been transformed to be at least sufficiently near or to reach a predetermined distribution, and estimate at least one likelihood of at least one predetermined result associated with the particular data as a function of the transformed data, whereas the repeated transformation ascends a likelihood function and/or is performed by unitary transformations.