  The importance sampling (IS) method lies at the core of many Monte
Carlo-based techniques. IS allows the approximation of a target probability
distribution by drawing samples from a proposal (or importance) distribution,
different from the target, and computing importance weights (IWs) that account
for the discrepancy between these two distributions. The main drawback of IS
schemes is the degeneracy of the IWs, which significantly reduces the
efficiency of the method. It has been recently proposed to use transformed IWs
(TIWs) to alleviate the degeneracy problem in the context of Population Monte
Carlo, which is an iterative version of IS. However, the effectiveness of this
technique for standard IS is yet to be investigated. In this letter we
numerically assess the performance of IS when using TIWs, and show that the
method can attain robustness to weight degeneracy thanks to a bias/variance
trade-off.
