Multidimensional Time Model for Probability Cumulative Function can be reduced to finite-dimensional time model,which can be characterized by Boolean algebra for operations over events and their probabilities and inde...Multidimensional Time Model for Probability Cumulative Function can be reduced to finite-dimensional time model,which can be characterized by Boolean algebra for operations over events and their probabilities and index set for reduction ofinfinite dimensional time model to finite number of dimensions of time model considering also the fractal-dimensional time arisingfrom alike supersymmetrical properties of probability. This can lead to various applications for parameter evaluation and riskreduction in such big complex data structures as complex dependence structures, images, networks, and graphs, missing and sparsedata, such as to computer vision, biology, medicine, and various DNA analyses.展开更多
文摘Multidimensional Time Model for Probability Cumulative Function can be reduced to finite-dimensional time model,which can be characterized by Boolean algebra for operations over events and their probabilities and index set for reduction ofinfinite dimensional time model to finite number of dimensions of time model considering also the fractal-dimensional time arisingfrom alike supersymmetrical properties of probability. This can lead to various applications for parameter evaluation and riskreduction in such big complex data structures as complex dependence structures, images, networks, and graphs, missing and sparsedata, such as to computer vision, biology, medicine, and various DNA analyses.
