prior probability 中文意思是什麼
prior probability
解釋
先驗概率- prior : n 普賴爾〈姓氏〉。adj 1 〈用作前置定[修飾]語〉在前的；優先的。2 〈與 to 連用〉在…之前 （opp post ...
- probability : n 1 或有；或然性。2 【哲學】蓋然性〈在 certainly 和 doubt 或 posibility 之間〉。3 【數學】幾率，...
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Compared with the regular rule - based expert system, the bayesian network based es can reason on the incomplete input information using the prior probability distribution ; the topological structure of the network being used to express the qualitative knowledge and the probability distributions of the nodes in the network being used to express the uncertainty of the knowledge, which made the knowledge representation more intuitively and more clearly ; applying the principle of the bayesian chaining rule, bidirectional inference which allow infer from the cause to the effect and from the effect to the cause can be achieved
與一般基於規則的專家系統相比，貝葉斯網專家系統利用先驗概率分佈，可以使推理在輸入數據不完備的基礎上進行；以網路的拓撲結構表達定性知識，以網路節點的概率分佈表達知識的不確定性，從而使不確定性知識的表達直觀、明確；利用貝葉斯法則的基本原理，可以實現由因到果及由果到因的雙向推理。 -
In addition, for general erlang ( n ) risk model, an integro - diifcrontial equation for the probability of ultimate ruin are presented : dickson arid hipp ( 2001 ) consider the erlang ( 2 ) risk model, and introduce the expectation of the discounted penalty h ' ( u ) which determines the joint and the marginal distribution of the time to ruin ( t ), the surplus prior to ruin ( u ( t - ) } and the deficit at ruin ( | u ( t ) | )
Dicksonandhipp （ 2001 ）同樣考慮了erlang （ 2 ）這種風險模型，並介紹了破產時的罰金折現期望w （ u ）這一概念。由罰金折現期望可得到破產時刻（ t ） ，破產前的瞬間盈餘（ u （ t ? ） ）和破產時的赤字（ u （ t ） ）的分佈和它們的聯合分佈，並給出了罰金折現期望滿足的一積分-微分方程，由此方程得到了罰金折現期望的拉普拉斯變換。 -
Monte carlo is a method that approximately solves mathematic or physical problems by statistical sampling theory. when comes to bayesian classification, it firstly gets the conditional probability distribution of the unlabelled classes based on the known prior probability. then, it uses some kind of sampler to get the stochastic data that satisfy the distribution as noted just before one by one
蒙特卡羅是一種採用統計抽樣理論近似求解數學或物理問題的方法，它在用於解決貝葉斯分類時，首先根據已知的先驗概率獲得各個類標號未知類的條件概率分佈，然後利用某種抽樣器，分別得到滿足這些條件分佈的隨機數據，最後統計這些隨機數據，就可以得到各個類標號未知類的后驗概率分佈。 -
The thesis mainly recounts the detail questions about bayesian small sample theory and the important applications of the theory in engineering, and gives sufficient analyses and discussion of every step of accomplishing a precision evaluation when using small samples. in the thesis, the following issues are contained, such as how to get and denote the prior information, the consistence test of prior information and test samples of shooting range, the fusion of multi - source information, calculating of posterior probability, estimation with bayesian approach, how to constitute test evaluation project of different performance and calculate the risks of both sides are contained, and at last a kind of applied method to calculate the effectiveness is given
論文主要敘述了有關bayes小樣本理論的一些具體問題，以及該技術在工程中的一些關鍵應用，對小樣本條件下精度鑒定的各個環節給予較充分的分析和討論，其中包括驗前信息的獲取、表示，驗前信息和靶場試驗樣本的一致性檢驗，多源信息的融合，驗后概率的計算， bayes方法在估計中的應用，試驗鑒定方案的制定，對不同戰標的評估方法和風險的計算等，最後對作戰效能的計算給出了一種工程中較實用的方法。 -
The main conclusions are following : ( 1 ) compared with the conventional mlc, the method of iterative prior probability based on the vector map can dispel the prior probability ’ s influence and the overall accuracy and kappa index can be improved ; ( 2 ) to the types with greater area than average area of all types, the producer ’ s accuracy will be improved while user ’ s accuracy be lessened, but to the ones with smaller area, the situation is just the opposite
本研究的主要結論是: （ 1 ）與傳統的最大似然法分類相比，利用地理數據矢量化得到的先驗概率進行迭代，可進一步消除先驗概率對最大似然分類法分類結果的影響，使分類總精度和kappa指數有進一步提高; （ 2 ）分佈面積大於平均值的類別，生產者精度一般會變高，使用者精度會變低;分佈面積小於平均值的類別，生產者精度一般會變低，使用者精度會變高。 -
In the sense of mean squares, maximum likelihood estimator, best linear unbiased estimator, taest linear invariant estimator, and good linear estimator are contracted. fourth, proposed and researched the reliability analysis method under the zero - failure data and doof data. based on the part beta distribution as the prior distribution of failure probability p, = p ( t < r, }, hierarchical bayesian estimate method was discussed, obtain the reliability analysis method under the zero - failure data and the doof data
第四，提出並研究了無失效數據類型和doof數據類型下電連接器的可靠性分析方法，提出了以不完全beta分佈為一級先驗分佈，超參數為[ 0 ， 1 ]上的均勻分佈作為失效概率先驗分佈的多層bayes方法，結合加權最小二乘法解決了產品在無失效數據和doof數據下的可靠性分析問題。 -
Comparing with non - bnyain methods, it ' s prominent featares lay in that it combines the prior and posterior information, which avoids the disadvantag of subjective bias caused by simply using the prior information only, of blind search caused by the incomplete sample information, of noise affection caused by simply using the sample information only if we choice a suitable priof, we can conduct the bayesian leaming effectively, so it fits the problems of data mining and machine leaming that possess charaters of probability and statistics, especially when the samples are rare
與非貝葉揚方法相比，貝葉斯方法的特出特點是其學習機制可以綜合先驗信息和后驗信息，既可避免只使用先驗信息可能帶來的主觀偏見，和缺乏樣本信息時的大量盲目搜索與計算，也可避免只使用樣本信息帶來的噪音的影響只要合理地確定先驗，就可以進行有效的學習。因此，適用於具有概率統計特徵的數據採掘和機器學習（或發現）問題，尤其是樣本難得的問題 -
Combined with the prior distribution of the model parameters and water quality observation data, joint posterior probability function which stands for the distribution characters was obtained by bayes ' theorem
結合模型參數的先驗分佈和水質監測數據，通過貝葉斯定理計算獲得了表徵參數分佈規律的聯合后驗概率密度函數。 -
At the same time, the previous global estimate of failure probability can serve as additional prior information to yield the overall calibrated probability
同時，先前估計的全局破壞概率，能夠用作額外的先驗信息，來給出全部校準的破壞概率。 -
The results show that bayes algorithm performs well in combining radar information for target identification because the need of prior probability is not too strict. but for bayes method, the robustness is not so well as that of d - s method
結果表明， bayes方法對先驗信息的精確程度要求並不十分嚴格，能較好地解決雷達情報綜合問題，而d - s方法比bayes方法更具有穩健性，但是其收斂時間較長。 -
In the process of decision with risks, if we using sampling theory, the best decision in prior probability and modified posteriori probability can be obtained
在進行風險型決策過程中，若能結合抽樣理論，就可以以最低的代價找到先驗概率下及修正後的后驗概率下選擇最優決策方案。 -
Finally, a new kind of methods on how to classify a sample into one of the several known populations in terms of posterior probability ratio established by the sample ' s predictive density functions when the unknown parameters " prior distributions are diffuse prior and minnesota prior or normal - inverted wishart distribution
最後，利用參數的充分統計量，根據后驗概率比構造了一類新的基於擴散先驗分佈和正態?逆wishart先驗分佈的多總體貝葉斯分類識別方法。 -
Supported by the analysis and advance process to the geographical data using gis software, the paper discusses the question that whether the accuracy of bayes supervised classification will be improved considering the influence of the prior probability
本文嘗試利用gis軟體對地理數據進行分析和預處理，對考慮先驗概率是否提高bayes監督分類精度這一問題作了探討。 -
The proportion based on the assistant data is used as the prior probability to replace the prior value in the conventional supervised classification ; the farther iterative prior probability is applied into classifying progress on landsat tm image
由輔助數據中計算各類別面積比率作為先驗概率，替換傳統監督分類中的先驗值，並進一步對先驗概率進行迭代，最後利用改進的先驗概率對landsattm影像進行分類實驗。 -
Kde is a non - parametric method which is capable of extracting the population ' s probability density function ( pdf ) based on data sample only without any a prior knowledge about the statistic properties of the data regime. in this thesis, it is conducted the implementation of the kde for monitoring the performance of batch production processes
用核函數法概率密度估計對間歇生產過程進行實時狀態監測的主要優點是它屬于非參數法概率密度估計的一種，不需要數據總體的任何先驗知識或是假設而直接基於實測數據樣本求出總體的概率分佈密度函數，擺脫了對不可靠的先驗知識的依賴。 -
Secondly, revise factor coefficient with probability distribution, which given by experienced experts. thirdly, use bayes statistic deducing method to bind together the income rate of prior distribution and sample in formation, which makes forecast stocks in shenzhen stock market as samples. work out the series of weakly income rate
（ 2 ）對多元回歸的因子模型的各因子權重重做修正，將一些對金融市場有較透徹了解和豐富經驗的專家提供的信息引入，作出因子系數的概率分佈（並非隨意的主觀臆造） ，對模型的結果加以修正，以便提高模型的準確度。 -
The algorithm needs no prior probability distributing knowledge of measurement data, and is easy to realize with simple programming and calculation
該演算法不要求知道測量數據的任何先驗概率分佈知識，編程簡單，計算量小。 -
It is popularly considered that considering the influence of the prior probability accords with bayes rule and ensures the minimal loss in the classifying progress
一般認為，考慮先驗概率符合bayes準則，能夠保證分類過程中錯分的損失最小。
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