There are several other families of MMSE lower bounds that could potentially be generalized in a similar way to handle discrete and mixed distributions. One such family is the Weiss-Weinstein family, which includes the Cramér-Rao bound and the Bhattacharyya bound. By extending these bounds to accommodate discrete and mixed distributions, researchers could compare their performance with the Ziv-Zakai bounds in various scenarios. Additionally, the maximum entropy bound, which is based on the principle of maximum entropy, could also be generalized to handle discrete and mixed distributions. Comparing the performance of these generalized bounds with the Ziv-Zakai bounds in different noise regimes and distribution types could provide valuable insights into the strengths and limitations of each approach in estimation problems.