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Theory of uncertain probability: can we derive the probability density function of uncertain random experiments with continuously changing conditions?
arXiv Math
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이 매체는 공공·자유 라이선스로 본문을 직접 표시합니다.Mathematics > Probability
[Submitted on 18 Jun 2026]
Title:Theory of uncertain probability: can we derive the probability density function of uncertain random experiments with continuously changing conditions?
View PDF HTML (experimental)Abstract:This paper aims to explore the formation mechanism of probability distribution in situations where the differences among random experiments are distinguishable, and these differences continue to evolve along with the dynamic changes in conditions and their mechanisms of action. To this end, we are motivated to devise a new theoretical system -- theory of uncertain probability (TUP) with Kolmogorov's system and nonlinear theories as special cases. TUP develops a novel model that integrates probability and uncertainty as well as the known and unknown to more accurately depict numerous typical random phenomena under more realistic assumptions, and thus provides appropriate tools for greater variety of real needs. It also allows for pioneering interpretation of the causal mechanisms underlying many important distributional characteristics and incorporation of pathwise property to distribution model.
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