Main impetus behind development of Bayesian analysis?

Faster way to do models of evolution and use statistical approaches, past was computationally intense(?)

Discrete data, clustering

Not a true optimality method

optimizing models of evolution and parameters

Advantages of bayesian analyses

faster than a maximum likelihood approach

not only a result but also an estimate on how certain I am, able to put a probability score on a node

has become primary method of figuring out phylogenetic relationships

parsimony eventually find wrong tree

long branch attraction ^: tends to cluster two branches together than evolve rapidly

L= Prob (Hypothesis|Data)

Our hypothesis is the tree and the data is analyzed.

Start w/ initial hypothesis

Initial understanding is is going to influence guess.

Accuracy

how close is it to the true tree?

Precision

how certain are you? How narrow is our range on our true and correct tree?

How can we know if our methods are accurately determining historical phylogenetic relationships?

We can never be sure we are entirely accurate. Simulated data sets that methods are able to recover can help us be sure and confident in accuracy of results + as well as known phylogenies like bacteria.

congruence between trees

Prior Probability

if I go into an analysis with some information then I have more power to help inform my subsequent analyses.

Posterior Probability

after initial test, becomes prior probability for new rounds of analyses.

Burn- in period

analyses eventually reaches a plateau

red ones, getting results out, stepping stones for getting better estimates

MCMC

short cut to speed up all estimations we have to do, aka when too complex.

Quick way to simulate a complex search space.

using elevation to figure out if a good solution or a bad solution.

allow to sample search topography, might end up isolated so repeated to find best solution

plateau period

blue one, these trees are saved

What is the final outcome of a bayesian analysis and how is this represented in a phylogeny?

results in lots of trees from the plateau phase, consensus tree / majority rule tree created, does the estimation faster than maximum likelihood method, but also gives us a number to show how often those relationships were there / how certain we are of those relationships.

"support values" ^^

Neighbor joining

starting point (?)

Posterior probability values

???

Congruence

do two phylogenies match?

What is the best method to find the phylogenetic relationships of a group and what does congruence have to do with this?

Congruence of methods shows accurate representation of analysis.

when bayesian analysis is done, part of process gives us support values

can be confident in results when compiled with parsimony and maximum likelihood analyses

when unsure, taxon sampling or increase data set!

Taxon sampling

what species do I choose to put into my analysis?

best strategy is - what group are we working with?