Given a one-dimensional posterior distribution it is often the case that you want to calculate a point estimate and a credible interval for the corresponding parameter. There are, of course, many ways of doing this, but there seem to be a couple of methods that are most often used in practice such as modes with highest posterior intervals and medians with equi-tailed intervals. There are however other proposed methods (for example, Snelson and Ghahramani (2005) and Druilhet and Marin (2007) ).
Is seems like "different methods for constructing posterior point/interval estimates" would be a perfect topic for a review paper/book and my question is: Does such a review exist?
(I've often seen questions regarding "what type of credible interval to use?" where the unhelpful answer is something like: "It depends on your loss function". So my question could equally well be: Does it exist a review of different loss functions for constructing posterior point/interval estimates?)
Snelson, E., & Ghahramani, Z. (2005). Compact approximations to Bayesian predictive distributions. In Proceedings of the 22nd international conference on Machine learning (pp. 840-847). ACM.
Druilhet, P., & Marin, J. M. (2007). Invariant HPD credible sets and MAP estimators. Bayesian Analysis', 2(4), 681-691.