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Activity Number: 308
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #318646 View Presentation
Title: Applications of Multidimensional Time Model for Probability Cumulative Function for Parameter Evaluation and Risk Reduction
Author(s): Michael Fundator*
Keywords: finite-dimensional time model ; normally distributed ; Chebyshev-Hermite polynomials

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 of infinite dimensional time model to finite number of dimensions of time model considering the fractal-dimensional time that is arising from alike supersymmetrical properties of probability. This can lead to various applications for parameter evaluation and risk reduction in such big complex data structures as complex dependence structures, images, networks, and graphs, missing and sparse data, such as various DNA analyses.

Authors who are presenting talks have a * after their name.

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