When: 22 April, 1-2 pm
Where: This seminar is given online. E-mail Dan Hedlin if you want to attend.


Nowadays, highly efficient algorithms for selecting samples from the most popular distributions are available. Nevertheless, the problem of sampling from any prescribed is yet to be fully resolved, especially when sampling from multidimensional distributions.

It is true that a large number of methods to sample from prescribed distributions –either exactly or approximately- are available (e.g. the inverse-transform method, the acceptance-rejection method, the Gibbs sampling or the Metropolis-Hastings sampling). Nevertheless, all have advantages and disadvantages.

We introduce an algorithm that allows for approximating a density by a mixture of easy-to-sample distributions. We illustrate the method with several examples and compare it with some widely used methods.

This presentation is part of ongoing research as part of Edgar Bueno's doctoral thesis.