Percolation probability functions for an infinite related model 一類無窮相關(guān)滲流模型的滲流概率函數(shù)
In the next article in this series , i ll implement some probability functions using native php code , extend the 在本系列文章的下一篇文章中,我將使用本機(jī)php代碼實(shí)現(xiàn)一些概率函數(shù),用幾個(gè)輸出方法擴(kuò)展
Probabilistic neural network ( pnn ) is a classification network , which is based on bayesian decision theory and probability function estimation theory D . f . specht提出的概率神經(jīng)網(wǎng)絡(luò)( probabilisticneuralnetwork , pnn )是基于密度函數(shù)估計(jì)和貝葉斯決策理論而建立的一種分類網(wǎng)絡(luò)
Because the basic probability function could not be gotten analytically , a fuzzy system replaces it and the fuzzy rules ' consequent parameters are gotten by the least squares method based on sampling data 由于基本可信度函數(shù)不能解析獲得,用一個(gè)模糊系統(tǒng)對其予以等價(jià),模糊規(guī)則后件參數(shù)由樣本數(shù)據(jù)通過最小二乘法獲得。
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(jié)合模型參數(shù)的先驗(yàn)分布和水質(zhì)監(jiān)測數(shù)據(jù),通過貝葉斯定理計(jì)算獲得了表征參數(shù)分布規(guī)律的聯(lián)合后驗(yàn)概率密度函數(shù)。