scholarly journals Monte Carlo simulation and statistical analysis of genetic information coding

2005 ◽  
Vol 357 (3-4) ◽  
pp. 525-533 ◽  
Author(s):  
E. Gultepe ◽  
M.L. Kurnaz
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Analysis ◽  
Genetic Information ◽  
Information Coding

Statistical analysis and Monte Carlo simulation of growing self-avoiding walks on percolation

2005 ◽  
Vol 345 (1-3) ◽  
pp. 197-204
Author(s):  
Yu-Xia Zhang ◽  
Jian-Ping Sang ◽  
Xian-Wu Zou ◽  
Zhun-Zhi Jin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Analysis ◽  
Self Avoiding Walks

Probabilistic and statistical analysis of mode stirred reverberation chamber and its Monte Carlo simulation

2011 ◽  
Vol 23 (9) ◽  
pp. 2475-2480
Author(s):  
张华彬 Zhang Huabin ◽  
赵翔 Zhao Xiang ◽  
周海京 Zhou Haijing ◽  
黄卡玛 Huang Kama
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Analysis ◽  
Reverberation Chamber

scholarly journals An analysis of a presumed major outbreak of pseudorabies virus in a vaccinated sow herd

2001 ◽  
Vol 126 (1) ◽  
pp. 119-128
Author(s):  
A. VAN NES ◽  
M. C. M. DE JONG ◽  
A. J. KERSTEN ◽  
T. G. KIMMAN ◽  
J. H. M. VERHEIJDEN
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Analysis ◽  
Monte Carlo Simulations ◽  
Mathematical Analysis ◽  
Generalized Linear Model ◽  
Pseudorabies Virus ◽  
Reproduction Ratio ◽  
Multiple Introductions ◽  
Major Outbreak

We describe a major outbreak of pseudorabies virus (PRV) in a sow herd in which the sows were vaccinated simultaneously three times a year with a vaccine containing Bartha strain. Also in the associated rearing herd in which the gilts were vaccinated twice an outbreak of PRV occurred. The outbreak was analysed with mathematical models, statistical methods and Monte-Carlo simulation. Under the assumption that the outbreak started with one introduction of virus the reproduction ratio Rind – as a measure of transmission of PRV between individuals – in the sow herd was estimated with a Generalized Linear Model to be 1·6. Also under the assumption of one introduction of virus Rind in the rearing herd was estimated with a martingale estimator to be 1·7. Both estimates were significantly larger than 1. Mathematical analysis showed that heterogeneity in the sow herd, because of the presence of not-optimally immunized replacement sows could not be the only cause of the observed outbreak in the sow herd. With Monte-Carlo simulations, the duration of an outbreak after a single introduction of virus and Rind = 1·6 did not mimic the data and thus the hypothesis of a single introduction with Rind = 1·6 could also be rejected and Rind is thus, not necessarily above 1. Moreover, with statistical analysis, endemicity in the combination of herds as a cause for the observed outbreak could be rejected. Endemicity in the rearing herd alone could not be excluded. Therefore, multiple introductions from outside and most probably from the rearing herd were possibly the cause of the observed outbreak(s). The implications for eradication of pseudorabies virus were discussed.

Computer simulation of marriage seasonality

1998 ◽  
Vol 10 (1-3) ◽  
pp. 10-16
Author(s):  
Manfred Brod
Keyword(s):  
Computer Simulation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Analysis ◽  
Event Data ◽  
Raw Data ◽  
Simple Statistical Analysis

Marriage seasonality in a given parish is subject to considerable year-on-year variation, but the corrections that need to be applied to the raw data are such as to make simple statistical analysis difficult. This can be overcome by the use of Monte Carlo simulation. The method provides simple criteria for deciding when a difference in seasonality between neighboring parishes or within a parish at different times may be regarded as significant. It may be applied generally in the analysis of event data where a mixture of numerical and logical criteria applies.

Statistical Analysis and Optimal Allocation of Geometric Tolerances

1995 ◽  
Author(s):  
Ashraf O. Nassef ◽  
Hoda A. ElMaraghy
Keyword(s):  
Monte Carlo Simulation ◽  
Genetic Algorithms ◽  
Monte Carlo ◽  
Statistical Analysis ◽  
Variance Reduction ◽  
Optimal Allocation ◽  
Variance Reduction Techniques ◽  
Reduction Techniques ◽  
Geometric Tolerances ◽  
Geometric Deviations

Abstract This paper describes a procedure for the statistical analysis and optimization of geometric tolerances. The proposed procedure assumes that a manufactured surface is represented by a set of points, which are assumed to be random variables having a multinormal distribution. Sets of surface points are generated from the multinormal distribution, and the minimum deviation zone for the geometric deviations in each set is compared with the specified tolerances. A parametric surface is interpolated to the generated points representing the manufactured surface. Genetic algorithms and a Monte Carlo simulation routine which incorporates variance reduction techniques are used to evaluate the geometric deviations of the machined surface. A second routine, based on genetic algorithms, is used to allocate the tolerance values which keeps the part’s probability of rejection within a desired value. An example for simulating a cylindrical feature is presented and the results obtained from the algorithms using the proposed variance reduction techniques are compared with those obtained using simple Monte Carlo simulation. In addition the specified tolerance values are reallocated to achieve a desired probability of rejection.

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