g code,

mu=model$parameter$mean

mixing coefficients of the two components in this mixture were

0.14, obtained using the following code,

w=model$parameter$pro

tandard deviation of the components was 1.62, obtained using the

g code,

ma=sqrt(model$parameter$variance$sigmasq)

d on these two components, a Gaussian mixture density for this

was estimated using the following code,

e1=dnorm(x0,mean=mu[1],sd=sigma)

e2=dnorm(x0,mean=mu[2],sd=sigma)

*mixture1+w[2]*mixture2

e above code, mixture1 and mixture2 stand for two

nts. They were mixed using two mixing coefficients, i.e., w[1]

]. Note that in the above code, two components had an identical

If two components had different variances, they should be

pelled out,

e1=dnorm(x0,mean=mu[1],sd=sigma[1])

e2=dnorm(x0,mean=mu[2],sd=sigma[2])

*mixture1+w[2]*mixture2

amma mixture

mma mixture has been popular for analysing biology data with

continuous values. A Gamma component density is defined as

࣮ሺߙ, ߚሻൌ^{ߚఈݔఈିଵ݁ିఉ௫}

Γሺߙሻ

(2.13)