Figure 1.

Sigmoid shape comparison. Comparison of different integration settings for the three log Bayes factor estimators (left), showing how a sigmoidal shape with α=6.0 is closest to our flexible-increment approach, while α=10.0 yields a curve that slowly converges towards both ends of the integration interval. The constant-increment approach is clearly a too rude approximation of a path between the two models. Bidirectional errors for a sigmoidal shape of α=8.0 (middle), showing that such a curve yields large errors towards both priors and that a higher shape value would be preferred. Bidirectional errors for a sigmoidal shape of α=12.0 (right), showing that such a curve yields larger errors in the middle of the integration interval although nowhere near the errors towards the priors for α=8.0.