front 1 fitch optimization | back 1 down pass up pass assigning state to nodes and conditional states |
front 2 Generalized parsimony | back 2 infer phylogenies and calculate large data sets indentical changes (JC) |
front 3 Maximum likelihood | back 3 uses models of evolution and stratistical tests computationally intense discrete, optimality |
front 4 Likelihood = data | hypothesis | back 4 probability of data given a certain hypothesis |
front 5 Two ways to get a known phylogeny to test effectiveness of phylogenetic analyses | back 5 Use know phylogenies, ex. bacteria culture experiments simulate data sets |
front 6 3 types of statistical tests ML analysis can do ? | back 6 is the model valid? molecular clock - untrametric evo, chage rate = same, estimate divergence time and count up all mutations ? which tree is best? |
front 7 3 disadvantages of ML analyses | back 7 circularity - need a phylogeny to infer computationally intense difference in rate of evolution (heteroachy) - won't be realistic |
front 8 Six criteria for evaluating effectiveness | back 8 efficiency power consistency robustness falsifiability (testability) ideological? |
front 9 congruence of methods | back 9 When changes are relatively rare (data set not saturated) both methods support the same topology |