statistic inference 中文意思是什麼
statistic inference
解釋
統計推理-
According to the statistic inference method the weibull distribution is discovered to be the best one
通過統計推斷和對比，找出重慶地面最低氣溫年極值遵循的最佳漸近分佈韋伯分佈。 -
( 3 ) based on the analyses results of simple inference method and seismic focal mechanism and on the statistic results of measured geostress data, the direction of principal stress was derived and based on the analyses results of macro geological estimate and on the statistic results of measured geostress data the magnitude of principal stress was derived too. the influence of rapidly down - cutting of yellow river on geostress field of studied zone was discussed. then the evolution of geostress field accompanying with the down cutting of yellow river and was simulated with fem and the spatial distribution features of geostress were discussed
根據簡易推斷法、地震震源機制分析法以及地應力實測資料統計分析結果，綜合確定了工程區的主壓應力方向；運用地質宏觀判斷法並結合地應力實測資料的統計分析結果對地應力的量級進行了綜合評價，並進一步討論了黃河快速下切對研究區地應力場的影響；運用有限元法模擬了研究區地應力場的形成過程，並探討了地應力的空間分佈規律。 -
In the second chapter, we explicate the theoretical knowledge, bayes statistic approach, which be applied in the paper, we show the definition of the prior distribution and how to select the prior information, we show the relation of prior distribution, conditional distribution and posterior distribution, we also show statistical inference approach and the key of how to use bayes statistic approach
第二部分內容是本文應用理論知識的簡要闡述，介紹了貝葉斯統計方法的理論，分別說明了先驗信息的定義及如何獲取，后驗分佈、條件分佈和先驗分佈三者關系，統計推斷方法及貝葉斯統計方法應用的關鍵。第三部分內容是對坦克射擊學中外彈道學的修正理論作了簡要的介紹。 -
Abstract : on the basis of the experimental data of microstructure and strength for gray cast iron with high carbon equivalent, the adapted fuzzy neural network model of relationship between microstructure and strength for predicting the strength of gray cast iron has been developed by using adaptive neural - fuzzy inference method. comparing with the models based on multiple statistic analysis, fuzzy regression or generalized regression neural network, it shows better learning precision and generalization
文摘:以高碳當量灰鑄鐵組織-強度實驗數據為基礎，用自適應模糊推理方法，建立了灰鑄鐵強度自適應模糊神經網路預測模型，與多元線性回歸、模糊回歸和廣義回歸神經網路模型相比，該模型學習精度高且具有較好的泛化性。
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