Table 2.

Model comparison using path sampling (PS) and stepping-stone sampling (SS) for the pseudogenes data set

α	K	Q	BDE(PS)	BDE($\overline{\mathit{\text{PS}}}$)	BDE(SS)	PS-A	PS-M	$\overline{\mathbf{\text{PS}}}\mathbf{-}\mathbf{\text{A}}$	$\overline{\mathbf{\text{PS}}}\mathbf{\text{-M}}$	SS-A	SS-M
6.0	16.000	200	8.54	7.74	12.47	174.61	180.14	172.89	179.06	171.26	182.21
8.0			3.80	2.93	3.04	174.75	175.00	174.38	174.72	174.17	175.14
9.0			2.35	2.36	2.45	174.99	174.96	174.82	175.38	174.71	175.52
10.0			2.69	2.65	2.64	174.64	174.98	174.41	175.56	174.35	175.70
11.0			3.49	3.93	3.99	175.66	175.72	175.61	175.89	175.64	176.01
12.0			3.65	2.91	2.86	174.50	174.29	174.57	174.51	174.53	174.64
13.0			2.86	2.59	2.60	175.48	174.79	175.45	174.79	175.40	174.93

Pseudogenes data set. Log Bayes factor estimates for thendent model compared to the site-independent model using regular path sampling (PS; only using the last sample for each path step), path sampling using the mean of a collection of samples from each path step ($\overline{\text{PS}}$) and regular stepping-stone sampling (SS). Bidirectional checks, consisting of annealing (-A) and melting (-M) integrations, were performed for each log Bayes factor calculation, yielding a bidirectional error (BDE) for each estimator. α values indicate the shape of the sigmoid function used to construct a path between the two models. K indicates the number of path steps (or ratios for SS) used, while Q indicates the number of iterations to be performed per path step (or ratio for SS).