Data from: LoRaD: Marginal likelihood estimation with haste (but no waste)

The Lowest Radial Distance (LoRaD) method is a modification of the
recently-introduced Partition-Weighted Kernel method for estimating the
marginal likelihood of a model, a quantity important for Bayesian model
selection. For analyses involving a fixed tree topology, LoRaD improves
upon the Steppingstone or Thermodynamic Integration (Path Sampling)
approaches now in common use in phylogenetics because it requires sampling
only from the posterior distribution, avoiding the need to sample from a
series of ad hoc power posterior distributions, and yet is more accurate
than other fast methods such as the Generalized Harmonic Mean (GHM)
method. We show that the method performs well in comparison to the
Generalized Steppingstone method on an empirical fixed-topology example
from molecular phylogenetics involving 180 parameters. The
LoRaD method can also be used to obtain the marginal likelihood
in the variable-topology case if at least one tree topology occurs with
sufficient frequency in the posterior sample to allow accurate estimation
of the marginal likelihood conditional on that topology.