Stop right there and rethink! The normalization factor depends on the parameter
that you are maximizing over.
-pd
> On 21 Dec 2017, at 11:29 , Lorenzo Isella wrote:
>
> In the code, dbeta1 is the density of the beta distribution for
> shape1=shape2=shape.
> In the code, dbeta2 is the same quan
I answer my own question: I had overlooked the fact that the normalization
factor is also a function of the parameters I want to optimise, hence I
should write
dbeta2 <- function(x, shape){
res <- x^(shape-1)*(1-x)^(shape-1)/beta(shape, shape)
return(res)
}
after which the results ar
Dear All,
I need to fit a custom probability density (based on the symmetric beta
distribution B(shape, shape), where the two parameters shape1 and shape2
are identical) to my data.
The trouble is that I experience some problems also when dealing with the
plain vanilla symmetric beta distribution.
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