By Dey D. K., Kuo L., Sahu S. K.

This paper describes a Bayesian method of combination modelling and a style in line with predictive distribution to figure out the variety of parts within the combos. The implementation is completed by using the Gibbs sampler. the tactic is defined throughout the combinations of ordinary and gamma distributions. research is gifted in a single simulated and one genuine information instance. The Bayesian effects are then in comparison with the possibility process for the 2 examples.

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**Extra info for A Bayesian predictive approach to determining the number of components in a mixture distribution**

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F y ( y ) = l f x ( y ) + fx(-y) IU(Y) (b) I f y > O and e - X ~ ( x ) = y ,t h e n x = - a n y . -- Furthermore, P{z=O) = P{x

D y -- 1 -1 dx: 2&- 2y Thus fu(9) = *fX(x:l) = 2Yfx(Y2) ' ,1 Y>O 7 otherwise which represents Rayleigh density function (with X = 2cr2). 5-9 For both cases, f y ( y ) = 0 f o r y < 0. 0 and 1x1 = y, then xlSy, x2--y. f y ( y ) = l f x ( y ) + fx(-y) IU(Y) (b) I f y > O and e - X ~ ( x ) = y ,t h e n x = - a n y . -- Furthermore, P{z=O) = P{x

F y ( y ) = l f x ( y ) + fx(-y) IU(Y) (b) I f y > O and e - X ~ ( x ) = y ,t h e n x = - a n y . -- Furthermore, P{z=O) = P{x