Author(s): Michael Hubig, Holger Muggenthaler, and Gita Mall
A major problem arising frequently in Bayesian estimation (BE) is to cope with a shift (e.g. a bias) of the likelihood probability. This paper investigates the impact of a severely shifted continuous likelihood probability on the output of BE in the real numbers. The result can be interpreted as a classification of asymptotic conditional probability distributions induced on a bounded interval by the far tail of probability distributions. It can be applied to a wide range of probability distributions. A hypothetical example of BE in death time estimation in a homicide trial, which is designed similar to a real case, illustrates the practical relevance of the results.