Needle roller bearing of bayesian posterior density function is derived

Needle roller bearing of bayesian posterior density function is derived: set ょ prior information for normal distribution NKn. v吧,那幺啊先脸密度函数被甲( E) = - V2nv1{ 1 a exp2v; 一,只需5 + o; I = 1, 2, m type. ‘a’雨数个boss先脸审度。 ッカ先验密度函数的平均值,w甲先脸密度函数的方法差异,i个轴承序号,米甲轴承套数。 Auspicious X, which can be obtained with ょ sakura sake and density function カ { 1NE - 2 na4 + x的) | + ! 等- 2它们+ n) n = ln = 1, 2, - ,N; 我= 1,2,- ,m ( 8 - 11) Type in the fXx ( n) And 5) 甲x; 朝! 朕联合密度函数,等i和of甲n ( Gi, o? ) Both numerical and variance, which o} 已知、ょ未知，A; And S; 甲x; Both numerical and in poor, ni カ prior density function on the average numerical, v2 カ prior density function of the variance, x { n) 甲第i杨绛轴承语法上第n购量i个轴承序号,米甲釉承套数,n个数据序号,n个数据个数,k式( 8 - 12) : N+1k=2n 2 v- 的年代; - - - - - - ~,i = l 2米( 8 - 12) Type, v カ first face of density function of analytic difference, s; 甲x柝准差,i个轴承序号,米甲轴承套数。 I remember for bearing the serial number, m for bearing a cycle. i= 1,2,. ,m( 8 - 13) σ= NA =σ2 + v”, mB =我= 1,xσ+ n; V:， i=1,2,,mNc=s+Zx( n) +n,v2, n=_2,- ,N; 1 - = 12, and mn = 1 type, s, as the standard deviation of X, the average of the ni as a prior density function, v? As a prior density function of the variance, x ( n) For the first n data sets of bearings, I for bearing the serial number, I m for bearing a cycle, n for data serial number, number n for the data. By type ( 8 - 11) ~ ( 8 - 16) , f ( x( n) And 5) = k exp < - - - - - - 2 ( 452 - 25,B+C)> 我= 1,2》,m; n = 1, 2,, n ( 8 - 17) Type of f ( x( n) And 5) As the X; With the factor; The joint probability density function, zeta parameters to be estimated, x ( n) For the first n data sets of bearings, I for bearing the serial number, I m for bearing a cycle, n for data serial number, number n for the data, if k = hexp ( - - - - - - ( C - B14) ( 8 - 18) So f ( x,( n) And 5) =kz expi=1,2,. ,m; n = 1, 2,。 N ( 8 - 19) 2 x - 1, ( 5 - ) Type of f ( x( n) , lead) As the X; And the joint probability density function of zeta, zeta parameters to be estimated, x; ( n) For the first n data sets of bearings, I for bearing the serial number, I m for bearing a cycle, n for data serial number, number n for the data. X; The marginal distribution of 1/2 m, ( x,( n) ) =f( x; ( n) And 5) d5; = kzi = 1、2, m; n = 1, 2,。 ,。 N ( 8 - 20) ( 2πA） Type of f ( Xx( n) , lead) As the X; And the joint probability density function of zeta, zeta I parameters to be estimated, x: ( n) For the first n data sets of bearings, I for bearing the serial number, I m for bearing properties; N for the data serial number, n number for the data.