The nucleotide equilibrium parameters of the HYK model are naturally suited to incorporate the nucleotide bias found in the different sequence models (e.g. donor, codon, etc.). With a multiple sequence alignment as input, an intuitive extension to the ab initio Markov model is to use the preceding o bases from each input sequence to estimate the likelihood of the current nucleotide (where o is the order of the Markov model). For example, in Figure 4, estimating the probability of nucleotide C at "Ancestor 2" having evolved from nucleotide A at "Ancestor 1", should reflect the nucleotide equilibrium of D. melanogaster and D. simulans, given the o previous bases in the input alignment for the two species. Similarly, estimating the probability of the root ancestral base being A (Ancestor 1) should reflect the nucleotide equilibrium among all four species given the o previous nucleotides in the input alignment from all four species. If dv is the number of descendants at node v, the number of parameters is ∑v4dv+o+1 MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaaeqaqaaiabisda0maaCaaaleqabaGaemizaq2aaSbaaWqaaiabdAha2bqabaWccqGHRaWkcqWGVbWBcqGHRaWkcqaIXaqmaaaabaGaemODayhabeqdcqGHris5aaaa@3835@ (v enumerating over all nodes in the tree) leaving too many parameters to reliably estimate, given the current limits on training sizes.