There is nothing wrong with defensive sampling and I still consider the paper by Hesterberg (1995) an important contribution to the field. However, in practice, finding a defensive distribution with fat tails that manages to contribute to the defensive weight is challenging: using the prior is not necessarily a good solution in that most simulations from the prior have zero posterior densities…

]]>However, after reading about various calculation techniques, it still seems that using “defensive” importance sampling on a 50/50 mix of the posterior and prior distributions should give good results and doesn’t require any custom coding. It seems this idea has gotten rejected because I only see it in older articles. Do you know what’s wrong with it?

Thanks,

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