probabilistic sampling 中文意思是什麼
probabilistic sampling
解釋
概率抽樣- probabilistic : adj. 1. （天主教教義）蓋然論的，或然說的。2. 概率的，幾率的。
- sampling : n. 1. 取樣（品），取標（本）〈指行動或程序〉。2. 樣品，標本。3. 剽竊拼湊歌曲。
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Stochastic and uncertain performance of power systems is thoroughly studied with a probabilistic simulation method in this paper. based on modeling of element failure and dispatching measures, static, dynamic and integrated securities are analyzed, and hence operation states are quantitatively classified. in probabilistic static security assessment, sequential and non - sequential monte - carlo sampling techniques are applied considering time varying parameter and constraints
本文採用概率模擬方法，深入研究了電力系統運行中的隨機和不確定特性，在對元件隨機故障和調度控制措施建模的基礎上，對系統的靜態安全、動態安全和綜合安全進行概率評估，建立了電力系統運行狀態的量化分析模型。 -
The effects are on the probabilistic assessment of both scattering regularity and sampling size of the test s - n data. p - s - n curves are characterized by the scale and location parameters related s - n relations for the maximum value model. the materials constants of in the scale relations are given by the average s - n relations and the locations
曲線用極大值分佈的位置與尺度參量s - n關系曲線來表徵，尺度參量s - n關系曲線可表示成均值與位置s - n曲線的函數；均值曲線的材料常數應用最小二乘法求出，位置曲線參數通過極大值分佈的似然函數解出。 -
Another decision, which has to be made when conducting a marketing research, concerns the sampling plan. the two basic sampling methods in use today are probabilistic and non - probabilistic sampling
同時，執行市場研究時必須制定樣本計劃。當今使用的兩種基本的樣本方法是或然樣本與非或然樣本。 -
The pheromone - based parameterized probabilistic model for the aco algorithm is presented as the solution construction graph that the combinatorial optimization problem can be mapped on. based on the solution construction graph, the unified framework of the aco algorithm is presented. an iterative update procedure of the solutions distribution in the problem ' s probabilistic model is proposed, that will converge to the optimal solutions with probability one, then the minimum cross - entropy pheromone update rule is proposed to approximate the iterative update procedure by minimizing the cross - entropy distance and monte - carlo sampling
基於解空間參數化概率分佈模型，首先提出了一個以概率1收斂于最優解的解空間概率分佈的迭代更新過程，然後提出了通過最小化不同分佈間的交互熵距離以及蒙特卡洛采樣來逼近此迭代過程的最小交互熵信息素更新規則，接著分別給出了弧模式以及結點模式信息素分佈模型下的最小交互熵等式。
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