In this paper, we highlight the use of neutrosophic crisp sets theory [3,4] with the
classical probability distributions, particularly Poisson distribution, Exponential distribution and Uniform distribution, which opens the way for dealing with issues that follow the classical distributions and at the same time contain data not specified accurately.
Formally, this is done by identifying two "research gaps" in the Misesian literature--a lack of conceptual clarity in dealing with risk and uncertainty (1) and a lack of justification for the view that
classical probability theory is irrelevant when dealing with human action (2)--and trying to close them.
This similarity points to the relation between the
classical probability functions defined on the (q, p) phase space and the quantum mechanical expectation values obtained from the [mathematical expression not reproducible] operators acting on the complex wavefunctions [psi](q) representing our knowledge of the system, which in the end obey the classical equations of motion.
Section 2 of this paper will show how the quantum to
classical probability transition can be made explicit by introducing a new criterion for "full" decoherence.
However, as the
classical probability theory allows for delta function distributions, P can be as singular as [[delta].sup.(2)](z) = [delta](Rez)[delta](Imz) [21, 22].
Probability of NCS is a generalization of the
classical probability in which the chance that an event A = <[A.sub.1], [A.sub.2], [A.sub.3])> to occur is:
The content of most of the teaching modules fell within Introduction to and Basics of Probability and Statistics, covering definition and properties of probability, basics of descriptive and inferential statistics, discrete random variables, expected value,
classical probability distributions, and central limit theorem.
The topics include multi-object modeling and filtering, implementing
classical probability hypothesis density and cardinalized probability hypothesis density filters, joint tracking and sensor-bias estimation, exact closed-form multi-target filter, random finite set filters for superpositional sensors, and single-target sensor management.
Returning to the setting of
classical probability theory, it is a
The order effect argues that human's decision pattern violates a fundamental requirement of
classical probability theory: Pr(A [intersection] B | C) = Pr(B [intersection] A | C) which implies Pr(C | A [intersection] B) = Pr(C | B [intersection] A) according to Bayesian rule [9].
Define the real data distribution [c.sub.1], and the
classical probability density function is [c.sub.2].