Probability distribution for a stochastic model of the motion of ore during discharge

1971 ◽  
Vol 7 (1) ◽  
pp. 24-28
Author(s):  
V. R. Imenitov ◽  
V. F. Abramov ◽  
V. A. Gorbunov
Keyword(s):  
Probability Distribution ◽  
Stochastic Model

A new discrete distribution arising in a model of DNA replication

2001 ◽  
Vol 38 (03) ◽  
pp. 754-760 ◽  
Author(s):  
Richard Cowan
Keyword(s):  
Dna Replication ◽  
Probability Distribution ◽  
Stochastic Model ◽  
Discrete Distribution ◽  
Recent Theory

During DNA replication, small fragments of DNA are formed. These have been observed experimentally and the mechanism of their formation modelled mathematically. Using the stochastic model of Cowan and Chiu (1992), (1994), we find the probability distribution of the number of fragments. A new discrete distribution arises. The work has interest as an application of the recent theory on quasirenewal equations in Piau (2000).

Operational Risk Evaluation of an Ice Feature Impact

1990 ◽  
Vol 112 (1) ◽  
pp. 96-101
Author(s):  
A. B. Dunwoody
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Stochastic Model ◽  
Risk Evaluation ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Offshore Structure ◽  
Simple Stochastic Model ◽  
General Method

The risk of impact by a particular ice feature in the vicinity of an offshore structure or stationary vessel is of concern during operations. A general method is presented for calculating the risk of an impact in terms of the joint probability distribution of the forecast positions and velocities of the ice feature. A simple stochastic model of the motion of an ice feature is introduced for which the joint probability distribution of ice feature position and velocity can be determined as a function of time. The risk of an impact is presented for this model of the motion of an ice feature. Predictions of the distributions of the time until impact and the drift speed upon impact are also presented and discussed. Predictions are compared against results of a Monte Carlo simulation.

The limiting conditional probability distribution in a stochastic model of T cell repertoire maintenance

2010 ◽  
Vol 224 (2) ◽  
pp. 74-86 ◽  
Author(s):  
Emily R. Stirk ◽  
Grant Lythe ◽  
Hugo A. van den Berg ◽  
Gareth A.D. Hurst ◽  
Carmen Molina-París
Keyword(s):  
T Cell ◽  
Probability Distribution ◽  
Stochastic Model ◽  
Conditional Probability ◽  
T Cell Repertoire ◽  
Cell Repertoire ◽  
Conditional Probability Distribution

scholarly journals Establishment of a Stochastic Model for Sustainable Economic Flood Management in Yewa Sub-Basin, Southwest Nigeria

2016 ◽  
Vol 2 (12) ◽  
pp. 646-655 ◽  
Author(s):  
O.A Agbede ◽  
Oluwatobi Aiyelokun
Keyword(s):  
Probability Distribution ◽  
Stochastic Model ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Flood Management ◽  
Return Periods ◽  
Probability Models ◽  
Goodness Of Fit Test ◽  
Southwest Nigeria ◽  
Anderson Darling

Of all natural disasters, floods have been considered to have the greatest potential damage. The magnitude of economic damages and number of people affected by flooding have recently increased globally due to climate change. This study was based on the establishment of a stochastic model for reducing economic floods risk in Yewa sub-basin, by fitting maximum annual instantaneous discharge into four probability distributions. Daily discharge of River Yewa gauged at Ijaka-Oke was used to establish a rating curve for the sub-basin, while return periods of instantaneous peak floods were computed using the Hazen plotting position. Flood magnitudes were found to increase with return periods based on Hazen plotting position. In order to ascertain the most suitable probability distribution for predicting design floods, the performance evaluation of the models using root mean square error was employed. In addition, the four probability models were subjected to goodness of fit test besed on Anderson-Darling (A2) and Kolmogorov-Smirnov (KS). As a result of the diagnostics test the Weibul probability distribution was confirmed to fit well with the empirical data of the study area. The stochastic model  generated from the Weibul probability distribution, could be used to enhance sustainable development by reducing economic flood damages in the sub-basin.

A new discrete distribution arising in a model of DNA replication

2001 ◽  
Vol 38 (3) ◽  
pp. 754-760 ◽  
Author(s):  
Richard Cowan
Keyword(s):  
Dna Replication ◽  
Probability Distribution ◽  
Stochastic Model ◽  
Discrete Distribution ◽  
Recent Theory

During DNA replication, small fragments of DNA are formed. These have been observed experimentally and the mechanism of their formation modelled mathematically. Using the stochastic model of Cowan and Chiu (1992), (1994), we find the probability distribution of the number of fragments. A new discrete distribution arises. The work has interest as an application of the recent theory on quasirenewal equations in Piau (2000).

AN ANALYTICAL SOLUTION FOR THE STOCHASTIC MODEL OF A TURBULENT CASCADE

2006 ◽  
Vol 20 (20) ◽  
pp. 1247-1251 ◽  
Author(s):  
A. A. MASOUDI ◽  
P. AZIMI ANARAKI
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Analytical Solution ◽  
Stochastic Model ◽  
Langevin Equation ◽  
Probability Distribution Function ◽  
Operator Method ◽  
Planck Equation ◽  
Velocity Moments ◽  
Turbulent Cascade

In this paper we are going to derive the Fokker–Planck equation (FPE) which is based on the Langevin equation for the stochastic model of a turbulent cascade and then we exactly solve the FPE for a certain case and find an explicit form of the probability distribution function (PDF) by the operator method. Finally, by using this PDF, we calculate the velocity moments for this system.

Stochastic Model of Durability of Rolling Bearings Considering Assembly Innaccuracies

2013 ◽  
Vol 199 ◽  
pp. 165-169
Author(s):  
Jerzy Tomaszewski
Keyword(s):  
Probability Distribution ◽  
Stochastic Model ◽  
Rolling Bearing ◽  
Reliability Function ◽  
Rolling Bearings ◽  
Angular Misalignment ◽  
Safety Coefficient ◽  
Second Moments ◽  
Assembly Error ◽  
Random Nature

This paper presents a method of determining the reliability function of rolling bearings operating under conditions of assembly error. Both the random nature of the assembly errors and the hour-by-hour durability of the bearing in question were considered. The perturbation method of second moments was used to solve the problem. This method allows for determining the moments of first and second order of the probability distribution of the safety coefficient, which is understood as the ratio of the actual durability in hours to the predetermined bearing durability in hours. As an example, the method of determining the distribution of the durability coefficient of a rolling bearing was presented for a situation of angular misalignment of coupled shafts.

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