scholarly journals Fejér-type inequalities for Lipschitzian functions and their applications

Filomat ◽  
2017 ◽  
Vol 31 (14) ◽  
pp. 4531-4542 ◽  
Author(s):  
Kai-Chen Hsu
Keyword(s):  
Convex Function ◽  
Mathematical Expectation ◽  
Beta Function ◽  
Random Variable ◽  
Lipschitzian Functions

In this paper, we shall establish some Fej?r-type inequalities for L-Lipschitzian functions. These inequalities can connect with Fej?r inequality (1). Also, some applications to convex function, ?-th moment, mathematical expectation of a random variable and Euler?s Beta function are provided.

scholarly journals Analytical Relations for the Mathematical Expectation and Variance of a Standard Distributed Random Variable Subjected to the √X Transformation

2018 ◽  
Vol 63 (3) ◽  
pp. 215 ◽  
Author(s):  
P. Kosobutsky
Keyword(s):  
Distribution Function ◽  
Mathematical Expectation ◽  
Random Variable ◽  
Standard Distribution ◽  
Physical Variables ◽  
Analytical Relations

The mathematical expectation and the variance have been calculated for random physical variables with the standard distribution function that are transformed by functionally related direct quadratic, X2, and inverse quadratic, √X, dependences.

scholarly journals Evolution of teaching the probability theory based on textbook by V. P. Ermakov

2021 ◽  
Vol 11 (2) ◽  
pp. 300-314
Author(s):  
Tetiana Malovichko
Keyword(s):  
Probability Theory ◽  
19Th Century ◽  
Mathematical Expectation ◽  
Random Variable ◽  
Total Probability ◽  
Creative Development ◽  
Educational Literature ◽  
And Mathematics ◽  
Kyiv Polytechnic Institute ◽  
The 19Th Century

The paper is devoted to the study of what changes the course of the probability theory has undergone from the end of the 19th century to our time based on the analysis of The Theory of Probabilities textbook by Vasyl P. Ermakov published in 1878. In order to show the competence of the author of this textbook, his biography and creative development of V. P. Ermakov, a famous mathematician, Corresponding Member of the St. Petersburg Academy of Sciences, have been briefly reviewed. He worked at the Department of Pure Mathematics at Kyiv University, where he received the title of Honored Professor, headed the Department of Higher Mathematics at the Kyiv Polytechnic Institute, published the Journal of Elementary Mathematics, and he was one of the founders of the Kyiv Physics and Mathematics Society. The paper contains a comparative analysis of The Probability Theory textbook and modern educational literature. V. P. Ermakov's textbook uses only the classical definition of probability. It does not contain such concepts as a random variable, distribution function, however, it uses mathematical expectation. V. P. Ermakov insists on excluding the concept of moral expectation accepted in the science of that time from the probability theory. The textbook consists of a preface, five chapters, a synopsis containing the statements of the main results, and a collection of tasks with solutions and instructions. The first chapter deals with combinatorics, the presentation of which does not differ much from its modern one. The second chapter introduces the concepts of event and probability. Although operations on events have been not considered at all; the probabilities of intersecting and combining events have been discussed. However, the above rule for calculating the probability of combining events is generally incorrect for compatible events. The third chapter is devoted to events during repeated tests, mathematical expectation and contains Bernoulli's theorem, from which the law of large numbers follows. The next chapter discusses conditional probabilities, the simplest version of the conditional mathematical expectation, the total probability formula and the Bayesian formula (in modern terminology). The last chapter is devoted to the Jordan method and its applications. This method is not found in modern educational literature. From the above, we can conclude that the probability theory has made significant progress since the end of the 19th century. Basic concepts are formulated more rigorously; research methods have developed significantly; new sections have appeared.

A MEAN–VARIANCE BOUND FOR A THREE-PIECE LINEAR FUNCTION

2007 ◽  
Vol 21 (4) ◽  
pp. 611-621 ◽  
Author(s):  
Karthik Natarajan ◽  
Zhou Linyi
Keyword(s):  
Convex Function ◽  
Closed Form ◽  
Linear Function ◽  
Upper Bound ◽  
Random Variable ◽  
Expected Value ◽  
Variance Bound ◽  
The Mean ◽  
Mean Variance

In this article, we derive a tight closed-form upper bound on the expected value of a three-piece linear convex function E[max(0, X, mX − z)] given the mean μ and the variance σ2 of the random variable X. The bound is an extension of the well-known mean–variance bound for E[max(0, X)]. An application of the bound to price the strangle option in finance is provided.

scholarly journals Natural Test for Random Numbers Generator Based on Exponential Distribution

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 920 ◽  
Author(s):  
Tanackov ◽  
Sinani ◽  
Stanković ◽  
Bogdanović ◽  
Stević ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Mathematical Expectation ◽  
Random Number ◽  
Random Variable ◽  
Random Numbers ◽  
The Third ◽  
Atmospheric Noise ◽  
Constant E ◽  
Fourth Series ◽  
Natural Test

We will prove that when uniformly distributed random numbers are sorted by value, their successive differences are a exponentially distributed random variable Ex(λ). For a set of n random numbers, the parameters of mathematical expectation and standard deviation is λ =n−1. The theorem was verified on four series of 200 sets of 101 random numbers each. The first series was obtained on the basis of decimals of the constant e=2.718281…, the second on the decimals of the constant π =3.141592…, the third on a Pseudo Random Number generated from Excel function RAND, and the fourth series of True Random Number generated from atmospheric noise. The obtained results confirm the application of the derived theorem in practice.

On measures of non-degeneracy

1992 ◽  
Vol 29 (3) ◽  
pp. 733-739
Author(s):  
K. B. Athreya
Keyword(s):  
Convex Function ◽  
Branching Processes ◽  
Jensen’S Inequality ◽  
Random Variable ◽  
Jensen's Inequality ◽  
Unique Role

If φ is a convex function and X a random variable then (by Jensen's inequality) ψ φ (X) = Eφ (X) – φ (EX) is non-negative and 0 iff either φ is linear in the range of X or X is degenerate. So if φ is not linear then ψ φ (X) is a measure of non-degeneracy of the random variable X. For φ (x) = x2, ψ φ (X) is simply the variance V(X) which is additive in the sense that V(X + Y) = V(X) + V(Y) if X and Y are uncorrelated. In this note it is shown that if φ ″(·) is monotone non-increasing then ψ φ is sub-additive for all (X, Y) such that EX ≧ 0, P(Y ≧ 0) = 1 and E(X | Y) = EX w.p.l, and is additive essentially only if φ is quadratic. Thus, it confirms the unique role of variance as a measure of non-degeneracy. An application to branching processes is also given.

Asymptotic properties of the number of replications of a paired comparison

1974 ◽  
Vol 11 (1) ◽  
pp. 43-52 ◽  
Author(s):  
V. R. R. Uppuluri ◽  
W. J. Blot
Keyword(s):  
Paired Comparison ◽  
Beta Function ◽  
Asymptotic Properties ◽  
Random Variable ◽  
Incomplete Beta Function ◽  
Discrete Random Variable ◽  
World Series ◽  
Mean And Variance ◽  
The Mean ◽  
Made In

A discrete random variable describing the number of comparisons made in a sequence of comparisons between two opponents which terminates as soon as one opponent wins m comparisons is studied. By equating two different expressions for the mean of the variable, a closed form for the incomplete beta function with equal arguments is obtained. This expression is used in deriving asymptotic (m-large) expressions for the mean and variance. The standardized variate is shown to converge to the Gaussian distribution as m→ ∞. A result corresponding to the DeMoivre-Laplace limit theorem is proved. Finally applications are made to the genetic code problem, to Banach's Match Box Problem, and to the World Series of baseball.

More bounds on the expectation of a convex function of a random variable

1972 ◽  
Vol 9 (4) ◽  
pp. 803-812 ◽  
Author(s):  
Ben-Tal A. ◽  
E. Hochman
Keyword(s):  
Convex Function ◽  
Upper Bound ◽  
Random Variable ◽  
Independent Random Variables ◽  
Multivariate Distributions ◽  
Absolute Deviation ◽  
Additional Information ◽  
Wait And See ◽  
The Mean ◽  
Moment Spaces

Jensen gave a lower bound to Eρ(T), where ρ is a convex function of the random vector T. Madansky has obtained an upper bound via the theory of moment spaces of multivariate distributions. In particular, Madansky's upper bound is given explicitly when the components of T are independent random variables. For this case, lower and upper bounds are obtained in the paper, which uses additional information on T rather than its mean (mainly its expected absolute deviation about the mean) and hence gets closer to Eρ(T).The importance of having improved bounds is illustrated through a nonlinear programming problem with stochastic objective function, known as the “wait and see” problem.

scholarly journals Hermite-Hadamard, Jensen, and Fractional Integral Inequalities for Generalized P -Convex Stochastic Processes

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fangfang Ma ◽  
Waqas Nazeer ◽  
Mamoona Ghafoor
Keyword(s):  
Probability Theory ◽  
Convex Function ◽  
Stochastic Processes ◽  
Fractional Integral ◽  
Probabilistic Models ◽  
Random Variable ◽  
Expected Value ◽  
Integral Inequalities ◽  
Stochastic Convexity ◽  
Convex Stochastic Processes

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.

More bounds on the expectation of a convex function of a random variable

1972 ◽  
Vol 9 (04) ◽  
pp. 803-812 ◽  
Author(s):  
Ben-Tal A. ◽  
E. Hochman
Keyword(s):  
Convex Function ◽  
Upper Bound ◽  
Random Variable ◽  
Independent Random Variables ◽  
Multivariate Distributions ◽  
Absolute Deviation ◽  
Additional Information ◽  
Wait And See ◽  
The Mean ◽  
Moment Spaces

Jensen gave a lower bound to Eρ(T), where ρ is a convex function of the random vector T. Madansky has obtained an upper bound via the theory of moment spaces of multivariate distributions. In particular, Madansky's upper bound is given explicitly when the components of T are independent random variables. For this case, lower and upper bounds are obtained in the paper, which uses additional information on T rather than its mean (mainly its expected absolute deviation about the mean) and hence gets closer to Eρ(T). The importance of having improved bounds is illustrated through a nonlinear programming problem with stochastic objective function, known as the “wait and see” problem.

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