Hermite-Hadamard, Jensen, and Fractional Integral Inequalities for Generalized -Convex Stochastic Processes
Author(s) -
Fangfang Ma,
Waqas Nazeer,
Mamoona Ghafoor
Publication year - 2021
Publication title -
journal of mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.252
H-Index - 13
eISSN - 2314-4785
pISSN - 2314-4629
DOI - 10.1155/2021/5524780
Subject(s) - mathematics , convex function , convexity , bounded function , random variable , regular polygon , combinatorics , discrete mathematics , mathematical analysis , statistics , geometry , financial economics , economics
The stochastic process is one of the important branches of probability theory which deals with probabilistic models that evolve over time. It starts with probability postulates and includes a captivating arrangement of conclusions from those postulates. In probability theory, a convex function applied on the expected value of a random variable is always bounded above by the expected value of the convex function of that random variable. The purpose of this note is to introduce the class of generalized p -convex stochastic processes. Some well-known results of generalized p -convex functions such as Hermite-Hadamard, Jensen, and fractional integral inequalities are extended for generalized p -stochastic convexity.
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