inference n. 1.推理,推論;推斷,結(jié)論,論斷;含蓄,含意。 2.推斷的結(jié)果;(邏輯上的)結(jié)論。 speak from inference 推測說。 draw [make] an inference from ... 根據(jù)…下結(jié)論。 the deductive [inductive] inference 演繹[歸納]推理。
Fourthly , the design of fuzzy inference engine for satellite environment control in accordance to the fuzzy inference theory 第四,根據(jù)模糊推理原理設(shè)計了衛(wèi)星環(huán)境控制模糊推理機。
Experiments proved its efficiency and robustness . this recognition system introduced d - s inference theory in statistical pattern recognition , and implemented it by neural network 該系統(tǒng)充分結(jié)合了d ? s證據(jù)理論在不確定性推理方面的優(yōu)勢以及神經(jīng)網(wǎng)絡(luò)強大的非線性處理能力,實驗結(jié)果表明了這一形狀識別系統(tǒng)的有效性。
Abstract : the mathematical model of reliability design for shaft rigidity was established based on reliability inference theory . reliability calculation of cylinder shaft rigidity of press was proceeded and regularity of cylinder shaft rigidity reliability as the condition change was found 文摘:應(yīng)用可靠性設(shè)計相干理論建立了軸的剛度可靠性設(shè)計的數(shù)學(xué)模型,著重探討印刷機印刷滾筒軸的剛度可靠度的計算,并得出不同條件下印刷滾筒軸剛度可靠度的變化規(guī)律。
Furthermore , the bayesian inference theory about unrestricted and restricted var ( p ) model under the parameter ' s prior distributions is explored . the structure of minnesota conjugate prior distribution , its hyper - parameters and determination , and the bayesian theory about var ( p ) model under the special conjugate prior distribution are all analyzed in detail 其次,探討了非限制性和限制性var ( p )預(yù)測模型的貝葉斯推斷理論,系統(tǒng)地分析了著名的minnesota共軛先驗分布的結(jié)構(gòu)及其超參數(shù)的設(shè)置,以及該先驗分布下var ( p )模型的貝葉斯推斷。
This paper mainly deals with the multivariate bayesian inference theory used in the modern economical and management science . this includes the bayesian inference theory about three important kinds of linear models , including the single equation model , multiple equation model system and var ( p ) predictive model , and their application in economic forecasting and quality control , and also the design for the bayesian classification identification method among multiple populations 本文主要研究現(xiàn)代經(jīng)濟管理中的多元貝葉斯推斷理論,包括單方程模型、多方程模型系統(tǒng)和向量自回歸var ( p )模型的貝葉斯推斷理論及其在經(jīng)濟預(yù)測與質(zhì)量控制中的應(yīng)用,以及多總體的貝葉斯分類識別方法的構(gòu)造。
This thesis mainly discussed the theoretical basis of information fusion , the applications and state - of - the - art . then studied it ' s applications on target recognition . we introduced dempster - shafer inference theory and neural network techniques , which were two typical algorithms in information fusion in details 本文主要研究信息融合的理論基礎(chǔ)、相關(guān)應(yīng)用問題以及研究現(xiàn)狀和發(fā)展方向,討論了信息融合技術(shù)在目標(biāo)識別中的應(yīng)用問題以及相關(guān)算法,重點介紹了d ? s證據(jù)理論和神經(jīng)網(wǎng)絡(luò)技術(shù)這兩種融合算法。
On the strength of the square loss function , this part also defines the vector loss function and matrix loss function , and discusses the bayesian risk decision solutions about random vector parameter and random matrix parameter under these loss functions respectively . secondly , the bayesian inference theory about single equation model is explored 在單參數(shù)平方損失函數(shù)的基礎(chǔ)上,定義了向量損失函數(shù),利用向量化算子vec定義了矩陣損失函數(shù),并討論了這兩類損失函數(shù)下隨機向量參數(shù)和隨機矩陣參數(shù)的貝葉斯風(fēng)險決策解。