An evaluation of probabilistic approaches to inference to the best explanation

This paper presents results of computer simulations for a number of different probabilistic versions of inference to the best explanation (IBE), which are distinguished by the probabilistic measures used to identify the best explanation. Simulation results are presented which include cases involving ignorance of a catch-all hypothesis, uncertainty regarding the prior probability distribution over the remaining hypotheses, initial elimination of implausible hypotheses, and variations in the number of pieces of evidence taken into consideration. The results show that at least some versions of IBE perform very well in a wide range of cases. In particular, the results for all approaches remain very similar (or improve in some cases) when just the two hypotheses with the highest prior probabilities are retained and the rest are eliminated from consideration.