Investigation of the accuracy of models of light-induced gas diffusion by the direct statistical modeling method

1991 ◽  
Vol 25 (5) ◽  
pp. 774-780
Author(s):  
A. �. Bazelyan
Keyword(s):  
Statistical Modeling ◽  
Gas Diffusion ◽  
Modeling Method

EVALUATION OF THE RELIABILITY OF COMPLEX TECHNICAL RECONFIGURATION SYSTEMS BY STATISTICAL MODELING METHOD

2019 ◽  
pp. 84-88
Author(s):  
A.Yu. Kulakov
Keyword(s):  
Normal Distribution ◽  
Statistical Modeling ◽  
Quantitative Estimate ◽  
Failure Time ◽  
Modeling Method ◽  
System Failure ◽  
Similar System ◽  
Orbit Control ◽  
Reliability Characteristics ◽  
Complex Technical System

Goal. Assess the reliability of a complex technical system with periodic reconfiguration and compare the results obtained a similar system, but without reconfiguration. Materials and methods. In this article uses the method of statistical modeling (Monte Carlo) to assess the reliability of complex system. We using the normal and exponential distribution of failure time for modeling failures of system elements. Reconfiguration algorithm is the algorithm proposed for the attitude and orbit control system of spacecraft. Results. A computer program has been developed for assessing reliability on the basis of a statistical modeling method, which makes it possible to evaluate systems of varying complexity with exponential and normal distribution, as well as with and without periodic reconfiguration. A quantitative estimate of the reliability as a function of the probability of system failure is obtained. Conclusion. It has been demonstrated that a system with reconfiguration has the best reliability characteristics, both in the case of exponential and normal distribution of failures.

A statistical modeling method for estimating mortality and abundance of spawning salmon from a time series of counts

2008 ◽  
Vol 65 (1) ◽  
pp. 17-26 ◽  
Author(s):  
R Glenn Szerlong ◽  
David E Rundio
Keyword(s):  
Time Series ◽  
Statistical Modeling ◽  
Detection Rate ◽  
Coho Salmon ◽  
Modeling Method ◽  
Detection Rates ◽  
Abundance Estimate ◽  
Mortality Model ◽  
Abundance Estimates ◽  
Approximate Likelihood

We present a statistical modeling method for estimating mortality and abundance of spawning salmon from time-series counts that eliminates the need for separate information about mortality. We model arrival and mortality using differential equations, where mortality can be constant or changing linearly, and estimate mortality and abundance from counts using maximum likelihood when multiple estimates of detection rate are available. We also develop an approximate likelihood to estimate mortality and abundance when only a single value for detection rate is available or to estimate only mortality when detection rates are entirely unknown. We demonstrate our approach using counts of coho salmon (Oncorhynchus kisutch) where mortality, abundance, and detection were determined from tagging at a weir. Our model for nonconstant mortality produced mortality estimates that closely matched the empirical data and were robust to variation in other parameters. It also provided a better fit to the stream counts and a closer abundance estimate to the weir count than the constant mortality model. Monte Carlo simulations indicated that the approximate likelihood provided reasonable estimates of mortality over most of the ranges of parameters explored, particularly under the nonconstant mortality model, and produced relatively unbiased abundance estimates using a single value for detection.

scholarly journals Study of Economic Systems Using the Simulation-Based Statistical Modeling Method

2015 ◽  
Author(s):  
Aleksandr Mikhaylovich Batkovskiy ◽  
Alina Valerevna Konovalova ◽  
Elena Georgievna Semenova ◽  
Jaroslavovich Trofimets ◽  
Alena Vladimirovna Fomina
Keyword(s):  
Statistical Modeling ◽  
Modeling Method ◽  
Economic Systems ◽  
Simulation Based
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