Single-run gradient estimation via measure-valued differentiation

We show how single-run-based measure-valued differentiation gradient estimators can be obtained. The key idea is to apply a change of measure a posterior to the mathematical analysis of the derivative. From the point of view of the likelihood ratio method, we show that likelihood ratio type gradient estimators can be applied in situations where the mathematical conditions needed for applying a likelihood ratio analysis are not met. Index Terms--Gradient estimation, likelihood ratios, measure-valued differentiation, Markov chains.