While the number of samples in the data now appears to be sufficiently high to infer statistical significance, no such inference is warranted due to the small number of samples that are genuinely independent.
If h0 is dominant and if h is sufficiently short, approximate inference is possible due to the negligible effect of h1 to hL−1 on r, but this is not normally the case.
The inability to make clear phylogenetic inference is partly due to a lack of information for deep branching organisms in this lineage.
We are interested in computing the posterior distribution for the mixing proportions, p (θ | R, T ) ∝ ∑ Z p (R | T, Z ) p (Z | θ ) p. For very small datasets, it is possible to perform exact Bayesian inference in this model, however for any realistically sized problem, exact inference is impossible due to the combinatorial explosion of the number of possible solutions.
First, since a correlation exists between evolutionary rate and heteropecilly (Tables 1 and 2), improvements in phylogenetic inference could be due to the removal of fast evolving sites [ 12, 53].
This is due to the fact that perfect inference requires that each clade in the gene tree be inferred correctly.
Conventional object detection algorithms are not yet suitable to harness this unconstrained, massive visual data because they require laborious bounding box annotations for training and large scale inference is infeasibly slow due to model complexity.
Moreover, precise inference is more difficult due to incomplete annotation, along with different stringency criteria, customary thresholds to classify true and erroneous AS events, and various gene models used in different species.
DOI: http://dx.doi.org/10.7554/eLife.04640.004 Even in the presence of large training data and priors, network inference is a difficult problem due to the combinatorial complexity (i.e., exponential expansion of the parameter search space with linear increase of parameters).
The 2D-to-3D inference is intrinsically a challenging problem due to the loss of 3D information in projection to 2D frames.
