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http://dx.doi.org/10.5392/JKCA.2021.21.11.077

A Study on the War Simulation and Prediction Using Bayesian Inference  

Lee, Seung-Lyong ((주)심네트 경영전략본부 R&D 및 V&V연구소)
Yoo, Byung Joo ((주)심네트 경영전략본부 R&D 및 V&V연구소)
Youn, Sangyoun ((주)심네트 경영전략본부 R&D 및 V&V연구소)
Bang, Sang-Ho ((주)심네트 경영전략본부 R&D 및 V&V연구소)
Jung, Jae-Woong ((주)심네트 경영전략본부 R&D 및 V&V연구소)
Publication Information
The Journal of the Korea Contents Association / v.21, no.11, 2021 , pp. 77-86 More about this Journal
Abstract
A method of constructing a war simulation based on Bayesian Inference was proposed as a method of constructing heterogeneous historical war data obtained with a time difference into a single model. A method of applying a linear regression model can be considered as a method of predicting future battles by analyzing historical war results. However it is not appropriate for two heterogeneous types of historical data that reflect changes in the battlefield environment due to different times to be suitable as a single linear regression model and violation of the model's assumptions. To resolve these problems a Bayesian inference method was proposed to obtain a post-distribution by assuming the data from the previous era as a non-informative prior distribution and to infer the final posterior distribution by using it as a prior distribution to analyze the data obtained from the next era. Another advantage of the Bayesian inference method is that the results sampled by the Markov Chain Monte Carlo method can be used to infer posterior distribution or posterior predictive distribution reflecting uncertainty. In this way, it has the advantage of not only being able to utilize a variety of information rather than analyzing it with a classical linear regression model, but also continuing to update the model by reflecting additional data obtained in the future.
Keywords
Bayesian Inference; Markov Chain Monte Carlo; Combat Model; Posterior Distribution; Modeling & Simulation;
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