In recent years various methods of estimation about probability density function of random variable sequence that is independent and identically distributed ( abbreviated as iid . ) and large sample quality have been more discussed in research documentation )序列的概率密度函數(shù)的各種估計(jì)方法及其大樣本性質(zhì)的研究文獻(xiàn)中已有很多討論,研究得最多的還是密度函數(shù)的核估計(jì),如rosenblent , parzen , prakasarao和silverman等。
The obtained results in the paper are as follows : ( 1 ) the expansion of fourier series of orthogonal trigonometric polynomial for conditional mathematical expectation and function of random variable ; ( 2 ) the best approximation of trigonometric polynomial about another random variable for function of a random variable ; ( 3 ) the best approximation order of trigonometric polynomial for function of random variable 摘要獲得了如下結(jié)果: ( 1 )條件數(shù)學(xué)期望及隨機(jī)變量函數(shù)的三角多項(xiàng)式級(jí)數(shù)表達(dá); ( 2 )一個(gè)隨機(jī)變量關(guān)于另一個(gè)隨機(jī)變量的三角多項(xiàng)式的最佳逼近; ( 3 )隨機(jī)變量函數(shù)被隨機(jī)變量三角多項(xiàng)式最佳逼近的階。
In the traditional reliability model , the probability density function of random variable is important . so , in chapter 2 , the methods to get probability density function of engineering random variable are discussed . three methods , which include approximate method , monte - carlo simulation method and multiple parameter method , are compared by some examples . in chapter 3 , some non - probabilistic reliability models are discussed 在傳統(tǒng)的概率可靠性模型中,變量的概率密度函數(shù)的確定非常重要,因此在本文第二章中,主要著力討論工程隨機(jī)變量概率密度函數(shù)的求解方法,說(shuō)明原理,推導(dǎo)求解公式,并通過(guò)實(shí)際算例對(duì)近似解析法、 monte - carlo數(shù)字模擬法、多參數(shù)法三種方法進(jìn)行對(duì)比。