|
Neigbor-Joining (NJ)
|
Distance Matrix
|
Iterative clustering method based on the minimum-evolution criterion; the topology with the least total branch length is preferred at each step.
|
|
UPGMA
| |
Agglomerative hierarchical clustering based on the average linkage method.
|
|
Maximum Parsimony (MP)
|
Phylogenetic Feature Matrix
|
Alternative evolutionary trees are generated; the one that satisfies the parsimony optimal criterion is considered as the best estimation: under maximum parsimony, the preferred phylogenetic tree is the tree that requires the smallest number of evolutionary changes.
|
|
Maximum Likelihood (ML)
| |
Alternative evolutionary trees are generated; the probability of an evolutionary event at any given point on a tree is stochastically modelled: under maximum likelihood, the preferred phylogenetic tree is the one with the highest likelihood.
|
|
Markov Chain Monte Carlo (MCMC)
|
Both
|
Bayesian inference method; alternative evolutionary trees are generated combining a posterior distribution for a feature and a model of evolution, based on the prior for that feature and the likelihood of the data, generated by a multiple alignment: unlike MP and ML a set of equally optimal trees may be produced. MCMC simulation is used to sample trees towards a credible subset.
|
