On Extension of Joint Distribution Functions on Quantum Logics

2019 ◽  
Vol 59 (1) ◽  
pp. 274-291
Author(s):  
Ol’ga Nánásiová ◽  
Jarosław Pykacz ◽  
L’ubica Valášková ◽  
Karla Čipková
Keyword(s):  
Joint Distribution ◽  
Distribution Functions ◽  
Quantum Logics

scholarly journals Error Uncertainty Analysis in Planar Closed-Loop Structure with Joint Clearances

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1872
Author(s):  
Yushu Yu ◽  
Jinglin Li ◽  
Xin Li ◽  
Yi Yang
Keyword(s):  
Joint Distribution ◽  
Error Probability ◽  
Closed Loop ◽  
Open Loop ◽  
Distribution Functions ◽  
Positional Error ◽  
Loop Structure ◽  
Virtual Link ◽  
Joint Clearances ◽  
Probability Density Function Pdf

For planar closed-loop structures with clearances, the angular and positional error uncertainties are studied. By using the vector translation method and geometric method, the boundaries of the errors are analyzed. The joint clearance is considered as being distributed uniformly in a circle area. A virtual link projection method is proposed to deal with the clearance affected length error probability density function (PDF) for open-loop links. The error relationship between open loop and closed loop is established. The open-loop length PDF and the closed-loop angular error PDF both approach being Gaussian distribution if there are many clearances. The angular propagation error of multi-loop structures is also investigated by using convolution. The positional errors of single and multiple loops are both discussed as joint distribution functions. Monte Carlo simulations are conducted to verify the proposed methods.

Mass transportation problems with capacity constraints

1999 ◽  
Vol 36 (2) ◽  
pp. 433-445 ◽  
Author(s):  
S. T. Rachev ◽  
I. Olkin
Keyword(s):  
Joint Distribution ◽  
Bivariate Distribution ◽  
Capacity Constraints ◽  
Distribution Functions ◽  
Mass Transportation ◽  
Marginal Distributions ◽  
Transportation Problems ◽  
The Family ◽  
Additional Capacity ◽  
Additional Constraints

We exhibit solutions of Monge–Kantorovich mass transportation problems with constraints on the support of the feasible transportation plans and additional capacity restrictions. The Hoeffding–Fréchet inequalities are extended for bivariate distribution functions having fixed marginal distributions and satisfying additional constraints. Sharp bounds for different probabilistic functionals (e.g. Lp-distances, covariances, etc.) are given when the family of joint distribution functions has prescribed marginal distributions, satisfies restrictions on the support, and is bounded from above, or below, by other distributions.

scholarly journals Stochastic orderings induced by star-shaped functions

1991 ◽  
Vol 14 (4) ◽  
pp. 639-664
Author(s):  
Henry A. Krieger
Keyword(s):  
Real Line ◽  
Stochastic Dominance ◽  
Joint Distribution ◽  
Decomposition Theorem ◽  
Open Interval ◽  
Distribution Functions ◽  
Proper Subset ◽  
Stochastic Orderings ◽  
The Real ◽  
Decreasing Functions

The non-decreasing functions whicl are star-shaped and supported above at each point of a non-empty closed proper subset of the real line induce an ordering, on the class of distribution functions with finite first moments, that is strictly weaker than first degree stochastic dominance and strictly stronger than second degree stochastic dominance. Several characterizations of this ordering are developed, both joint distribution criteria and those involving only marginals. Tle latter are deduced from a decomposition theorem, which reduces the problem to consideration of certain functions which are star-shaped on the complement of an open interval.

scholarly journals Comments on Copula Functions and Their Relationship to Probability Density Functions

2020 ◽  
pp. 1115-1122
Author(s):  
Ahmed AL-Adilee ◽  
Ola Hassan
Keyword(s):  
Probability Density ◽  
Statistical Inference ◽  
Joint Distribution ◽  
Probability Density Functions ◽  
Distribution Functions ◽  
Density Functions ◽  
Copula Functions ◽  
Statistical Inferences ◽  
Dependence Structures

Copulas are very efficient functions in the field of statistics and specially in statistical inference. They are fundamental tools in the study of dependence structures and deriving their properties. These reasons motivated us to examine and show  various types of copula functions and their families. Also, we separately explain each method that is used to construct each copula in detail with different examples. There are various outcomes that show the copulas and their densities with respect to the joint distribution functions. The aim is to make copulas available to new researchers and readers who are interested in the modern phenomenon of statistical inferences.

scholarly journals PUT OPTION PRICES AS JOINT DISTRIBUTION FUNCTIONS IN STRIKE AND MATURITY: THE BLACK–SCHOLES CASE

2009 ◽  
Vol 12 (08) ◽  
pp. 1075-1090
Author(s):  
D. MADAN ◽  
B. ROYNETTE ◽  
M. YOR
Keyword(s):  
Distribution Function ◽  
Brownian Motion ◽  
Large Class ◽  
Joint Distribution ◽  
Distribution Functions ◽  
Standard Brownian Motion ◽  
Option Prices ◽  
Put Option ◽  
Black Scholes

For a large class of ℝ+ valued, continuous local martingales (Mtt ≥ 0), with M0 = 1 and M∞ = 0, the put quantity: ΠM (K,t) = E ((K - Mt)+) turns out to be the distribution function in both variables K and t, for K ≤ 1 and t ≥ 0, of a probability γM on [0,1] × [0, ∞[. In this paper, the first in a series of three, we discuss in detail the case where [Formula: see text], for (Bt, t ≥ 0) a standard Brownian motion.

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