## infinite mixtures are likely to take a while to simulate

**A**nother question on X validated got me highly interested for a while, as I had considered myself the problem in the past, until I realised while discussing with Murray Pollock in Warwick that there was no general answer: *when a density f is represented as an infinite series decomposition into weighted densities, some weights being negative, is there an efficient way to generate from such a density?* One natural approach to the question is to look at the mixture with positive weights, *f⁺*, since it gives an upper bound on the target density. Simulating from this upper bound *f⁺* and accepting the outcome x with probability equal to the negative part over the sum of the positive and negative parts *f⁻(x)*/*f(x)* is a valid solution. Except that it is not implementable if

- the positive and negative parts both involve infinite sums with no exploitable feature that can turn them into finite sums or closed form functions,
- the sum of the positive weights is infinite, which is the case when the series of the weights is not absolutely converging.

Even when the method is implementable it may be arbitrarily inefficient in the sense that the probability of acceptance is equal to to the inverse of the sum of the positive weights and that simulating from the bounding mixture in the regular way uses the original weights which may be unrelated in size with the actual importance of the corresponding components in the actual target. Hence, when expressed in this general form, the problem cannot allow for a generic solution.

Obviously, if more is known about the components of the mixture, as for instance the sequence of weights being alternated, there exist specialised methods, as detailed in the section of series representations in Devroye’s (1985) simulation bible. For instance, in the case when positive and negative weight densities can be paired, in the sense that their weighted difference is positive, a latent index variable can be included. But I cannot think of a generic method where the initial positive and negative components are used for simulation, as it may on the opposite be the case that no finite sum difference is everywhere positive.

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