We consider the problem of estimating the density of a random variable
which is the output of a simulation model.
We show how an unbiased density estimator can be constructed via
the classical likelihood ratio derivative estimation method proposed
over 35 years ago by Glynn, Rubinstein, and others.
We then extend this density estimation method to cover situations where it does not apply directly.
What we obtain is closely related to the generalized likelihood ratio method
proposed recently by Peng and his co-authors, although the assumptions differ.
We compare the methods and assumptions on some examples.
Published April 2022 , 15 pages
G2215.pdf (400 KB)