scholarly journals An Application of New Method to Obtain Probability Density Function of Solution of Stochastic Differential Equations

2020 ◽  
Vol 5 (1) ◽  
pp. 337-348 ◽  
Author(s):  
Nihal İnce ◽  
Aladdin Shamilov
Keyword(s):  
Probability Density Function ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Probability Density ◽  
Density Function ◽  
Model Fitting ◽  
Random Variable ◽  
Optimization Methods ◽  
Fixed Time ◽  
New Method

AbstractIn this study, a new method to obtain approximate probability density function (pdf) of random variable of solution of stochastic differential equations (SDEs) by using generalized entropy optimization methods (GEOM) is developed. By starting given statistical data and Euler–Maruyama (EM) method approximating SDE are constructed several trajectories of SDEs. The constructed trajectories allow to obtain random variable according to the fixed time. An application of the newly developed method includes SDE model fitting on weekly closing prices of Honda Motor Company stock data between 02 July 2018 and 25 March 2019.

scholarly journals Determining the First Probability Density Function of Linear Random Initial Value Problems by the Random Variable Transformation (RVT) Technique: A Comprehensive Study

2014 ◽  
Vol 2014 ◽  
pp. 1-25 ◽  
Author(s):  
M.-C. Casabán ◽  
J.-C. Cortés ◽  
J.-V. Romero ◽  
M.-D. Roselló
Keyword(s):  
Stochastic Process ◽  
Probability Density Function ◽  
Differential Equations ◽  
Probability Density ◽  
Density Function ◽  
Initial Value Problems ◽  
Random Variable ◽  
Initial Value ◽  
Variable Transformation ◽  
Comprehensive Study

Deterministic differential equations are useful tools for mathematical modelling. The consideration of uncertainty into their formulation leads to random differential equations. Solving a random differential equation means computing not only its solution stochastic process but also its main statistical functions such as the expectation and standard deviation. The determination of its first probability density function provides a more complete probabilistic description of the solution stochastic process in each time instant. In this paper, one presents a comprehensive study to determinate the first probability density function to the solution of linear random initial value problems taking advantage of the so-called random variable transformation method. For the sake of clarity, the study has been split into thirteen cases depending on the way that randomness enters into the linear model. In most cases, the analysis includes the specification of the domain of the first probability density function of the solution stochastic process whose determination is a delicate issue. A strong point of the study is the presentation of a wide range of examples, at least one of each of the thirteen casuistries, where both standard and nonstandard probabilistic distributions are considered.

On the linear exponential family

1968 ◽  
Vol 64 (2) ◽  
pp. 481-483 ◽  
Author(s):  
J. K. Wani
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Scalar Product ◽  
Exponential Family ◽  
Characterization Theorem ◽  
Random Variable ◽  
Moment Generating Function ◽  
The Moment ◽  
Image Position

In this paper we give a characterization theorem for a subclass of the exponential family whose probability density function is given bywhere a(x) ≥ 0, f(ω) = ∫a(x) exp (ωx) dx and ωx is to be interpreted as a scalar product. The random variable X may be an s-vector. In that case ω will also be an s-vector. For obvious reasons we will call (1) as the linear exponential family. It is easy to verify that the moment generating function (m.g.f.) of (1) is given by

A generalized Ostrowski type inequality for a random variable whose probability density function belongs to L∞[a, b]

2008 ◽  
Vol 41 (3) ◽  
Author(s):  
Arif Rafiq ◽  
Nazir Ahmad Mir ◽  
Fiza Zafar
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Type Inequality ◽  
Random Variable ◽  
Ostrowski Type Inequality

AbstractWe establish here an inequality of Ostrowski type for a random variable whose probability density function belongs to L

scholarly journals Research on Algorithm of Extracting PPG Signal for Detecting Atrial Fibrillation based on Probability Density Function

2015 ◽  
Vol 9 (1) ◽  
pp. 179-184 ◽  
Author(s):  
Kang Liang ◽  
Ying Sun ◽  
Fuying Tian ◽  
Shenghua Ye
Keyword(s):  
Atrial Fibrillation ◽  
Phase Space ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Normal Sinus Rhythm ◽  
New Method ◽  
Diagram Method ◽  
Normal Sinus ◽  
Probability Density Function Pdf

The paper introduced a new method based on probability density function (PDF) and phase space diagram method for photoplethysmography (PPG) signal extracting. In the paper, PPG information was generated from human fingertips by smartphones. The pulse wave period was then separated and reconstructed into probability density function (PDF) by the phase space diagram algorithm. The difference between normal sinus rhythm (NSR) and atrial fibrillation (AF) was finally found by skewness of the PDF. The results of the present study demonstrates that the new method is vividly viable for detecting AF on the smartphone.

Methods of Computing the Probability of Failure Under Extreme Values of Bending Moment

1972 ◽  
Vol 16 (02) ◽  
pp. 113-123
Author(s):  
Alaa Mansour
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Extreme Values ◽  
Bending Moment ◽  
Random Variable ◽  
Probability Of Failure ◽  
Short Term ◽  
Term Analysis

Methods for predicting the probability of failure under extreme values of bending moment (primary loading only) are developed. In order to obtain an accurate estimate of the extreme values of the bending moment, order statistics are used. The wave bending moment amplitude treated as a random variable is considered to follow, in general, Weibull distribution so that the results could be used for short-term as well as long-term analysis. The probability density function of the extreme values of the wave bending moment is obtained and an estimate is made of the most probable value (that is, the mode) and other relevant statistics. The probability of exceeding a given value of wave bending moment in "n" records and during the operational lifetime of the ship is derived. Using this information, the probability of failure is obtained on the basis of an assumed normal probability density function of the resistive strength and deterministic still-water bending moment. Charts showing the relation of the parameters in a nondimensional form are presented. Examples of the use of the charts for long-term and short-term analysis for predicting extreme values of wave bending moment and the corresponding probability of failure are given.

OPTIMAL CONTROL OF CHAOTIC SYSTEMS

2001 ◽  
Vol 11 (07) ◽  
pp. 2007-2018 ◽  
Author(s):  
PAWEŁ GÓRA ◽  
ABRAHAM BOYARSKY
Keyword(s):  
Optimal Control ◽  
Probability Density Function ◽  
Probability Measure ◽  
Probability Density ◽  
Density Function ◽  
Chaotic Systems ◽  
Invariant Probability Measure ◽  
Optimization Methods ◽  
Point Transformation

The problem of controlling a chaotic system is treated from a long term statistical basis. Unlike the OGY targeting method that exploits individual unstable orbits, this approach is concerned with targeting the density function of an invariant probability measure. Given a point transformation T, possessing a density function f, we choose [Formula: see text], a different probability density function, to be the target. Using optimization methods, we construct a point transformation [Formula: see text], close to T, whose invariant probability density function is [Formula: see text], or close to [Formula: see text].

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