The hypothesis of randomness is fundamental in statistical machine learning and many areas nonparametric statistics; it says that the observations are assumed to be independent coming from same unknown probability distribution. This close, certain respects, exchangeability, which postulates distribution invariant with respect their permutations. paper reviews known methods testing two hypothese...

Abstract We provide a methodology for eliciting utility midpoints from preferences, assuming that payoffs are consumption plans and preferences satisfy minimal form of additive separability. The does not require any subjective or objective uncertainty. Thus, this construction allows us to define mixtures acts in purely fashion, without making assumptions as the decision maker’s reaction uncerta...

Spatial distributions of biological species are an important source of information for understanding local interactions at the scale of individuals. Technological advances have made it easier to measure these distributions as spatial point patterns, specifying the locations of individuals. Extensive attention has been devoted to analyzing the second-order structure of such point patterns by foc...

A model which in the thermodynamic limit calculates the partition function exactly is used to study the phase transitions of systems with two interacting order parameters coupled to their respective random fields. An ordered phase is always unstable for the spatial range dø4 when both random fields are present. A continuous phase transition is possible when only one of the random fields is nonz...

We investigate the spatiotemporal dynamics of a network of coupled chaotic maps, with varying degrees of randomness in coupling connections. While strictly nearest neighbor coupling never allows spatiotemporal synchronization in our system, randomly rewiring some of those connections stabilizes entire networks at x*, where x* is the strongly unstable fixed point solution of the local chaotic ma...