Factor Screening in Simulation Experiments: Review of Sequential Bifurcation

2009 ◽  
pp. 153-167 ◽  
Author(s):  
Jack P. C. Kleijnen
Keyword(s):  
Simulation Experiments ◽  
Factor Screening

COGNITIVE MODELING AS A METHOD TO CONTROL MIGRATION TEMPERATURE

2020 ◽  
pp. 69-87
Author(s):  
Oleg Patlasov ◽  
Oleg Luchko ◽  
Svetlana Mukhametdinova
Keyword(s):  
Temperature Control ◽  
Cognitive Modeling ◽  
Statistical Data ◽  
Controlling Factors ◽  
Simulation Experiments ◽  
Approaches To Studying ◽  
Economic Problems ◽  
Qualitative And Quantitative ◽  
The Social ◽  
Expert Assessments

The research describes one of the approaches to designing a productive mechanism for migration temperature control considering it as an integral qualitative and quantitative indicator of the social and economic problems level associated with migration processes. The analysis of various approaches to studying migration processes impact on socioeconomic situation in recipient countries has been carried out. Some cognitive models have been developed basing on the questionnaire results’ analysis, expert assessments, statistical data. A series of simulation experiments have been carried out using software specially developed to automate the cognitive modeling processes.In the course of our experiments, some changes in the target factor. i.e., in migration temperature, have been detected as a result from different intensity impulses impacting on individual controlling factors. Within the developed models framework, several proposals have been put forward concerning the productive mechanism for migration temperature control.

State Estimation Using Randomly Delayed Measurements

1993 ◽  
Vol 115 (1) ◽  
pp. 19-26 ◽  
Author(s):  
A. Ray ◽  
L. W. Liou ◽  
J. H. Shen
Keyword(s):  
State Estimation ◽  
Minimum Variance ◽  
The State ◽  
Sensor Data ◽  
State Estimator ◽  
Uniform Asymptotic Stability ◽  
Simulation Experiments ◽  
State Estimate ◽  
Sampling Instant ◽  
Recursive Relations

This paper presents a modification of the conventional minimum variance state estimator to accommodate the effects of randomly varying delays in arrival of sensor data at the controller terminal. In this approach, the currently available sensor data is used at each sampling instant to obtain the state estimate which, in turn, can be used to generate the control signal. Recursive relations for the filter dynamics have been derived, and the conditions for uniform asymptotic stability of the filter have been conjectured. Results of simulation experiments using a flight dynamic model of advanced aircraft are presented for performance evaluation of the state estimation filter.

scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

scholarly journals A regularized hidden Markov model for analyzing the ‘hot shoe’ in football

2021 ◽  
pp. 1471082X2110080
Author(s):  
Marius Ötting ◽  
Groll Andreas
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Hot Hand ◽  
Special Cases ◽  
Psychology And Economics ◽  
Likelihood Approach ◽  
Penalized Likelihood Approach ◽  
Hot Hand Effect

We propose a penalized likelihood approach in hidden Markov models (HMMs) to perform automated variable selection. To account for a potential large number of covariates, which also may be substantially correlated, we consider the elastic net penalty containing LASSO and ridge as special cases. By quadratically approximating the non-differentiable penalty, we ensure that the likelihood can be maximized numerically. The feasibility of our approach is assessed in simulation experiments. As a case study, we examine the ‘hot hand’ effect, whose existence is highly debated in different fields, such as psychology and economics. In the present work, we investigate a potential ‘hot shoe’ effect for the performance of penalty takers in (association) football, where the (latent) states of the HMM serve for the underlying form of a player.

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