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# Presentation taken from Nir Friedman’s HU course, available at cs.huji.ac.il/~pmai . - PowerPoint PPT Presentation

Maximum Likelihood ( ML ) Parameter Estimation with applications to inferring phylogenetic trees Comput. Genomics, lecture 6a. Presentation taken from Nir Friedman’s HU course, available at www.cs.huji.ac.il/~pmai . Changes made by Dan Geiger, Ydo Wexler, and finally by Benny Chor.

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### Maximum Likelihood (ML) Parameter Estimation with applications to inferring phylogenetic treesComput. Genomics, lecture 6a

Presentation taken from Nir Friedman’s HU course, available at www.cs.huji.ac.il/~pmai.

Changes made by Dan Geiger, Ydo Wexler, and finally by Benny Chor.

.

• We have a probabilistic model,M, of some phenomena. We know exactly the structure of M, but not the values of its probabilistic parameters, .

• Each “execution” of M produces an observation, x[i] , according to the (unknown) distribution induced by M.

• Goal: After observing x[1] ,…, x[n] , estimate the model parameters, , that generated the observed data.

• The likelihood of the observed data, given the

model parameters, as the conditional

probabilitythat the model, M, with parameters

, produces x[1] ,…, x[n] .

L()=Pr(x[1] ,…, x[n] | , M),

• In MLE we seek the model parameters, , that

maximize the likelihood.

• In MLE we seek the model parameters, , that

maximize the likelihood.

• The MLE principle is applicable in a wide

variety of applications, from speech recognition,

through natural language processing, to

computational biology.

• We will start with the simplest example:

Estimating the bias of a coin. Then apply MLE

to inferring phylogenetic trees.

• (will later talk about MAP - Bayesian inference).

• When tossed, it can land in one of two positions: Head(H) or Tail (T)

Tail

• We denote by  the (unknown) probability P(H).

• Given a sequence of toss samples x[1], x[2], …, x[M] we want to estimate the probabilities P(H)= and P(T) = 1 - 

Samples

(why??)

Statistical Parameter Fitting (restement)

• Consider instances x[1], x[2], …, x[M]

such that

• The set of values that x can take is known

• Each is sampled from the same distribution

• Each sampled independently of the rest

• The task is to find a vector of parameters

 that have generated the given data. This

vector parameter  can be used to predict

future data.

L()

0

0.2

0.4

0.6

0.8

1

The Likelihood Function

• How good is a particular ?It depends on how likely it is to generate the observed data

• The likelihood for the sequence H,T, T, H, H is

• To compute the likelihood in the thumbtack example we only require NH and NT (the number of heads and the number of tails)

• NH and NT are sufficient statistics for the binomial distribution

• Formally, s(D) is a sufficient statistics if for any two datasets D and D’

• s(D) = s(D’ ) LD() = LD’ ()

Datasets

Statistics

Sufficient Statistics

• A sufficient statistic is a function of the data that summarizes the relevant information for the likelihood

MLE Principle:

Choose parameters that maximize the likelihood function

• This is one of the most commonly used estimators in statistics

• Intuitively appealing

• One usually maximizes the log-likelihood function, defined as lD() = ln LD()

L()

0

0.2

0.4

0.6

0.8

1

Example:

(NH,NT ) = (3,2)

MLE estimate is 3/5 = 0.6

Example: MLE in Binomial Data

Taking derivative and equating it to 0,

we get

(which coincides with what one would expect)

• N1, N2, …, NK - the number of times each outcome is observed

Likelihood function:

MLE: (proof @ assignment 3)

From Binomial to Multinomial

• Now suppose X can have the values 1,2,…,K(For example a die has K=6 sides)

• We want to learn the parameters 1, 2. …, K

MLE:

Example: Multinomial

• Let be a protein sequence

• We want to learn the parameters q1, q2,…,q20

corresponding to the frequencies of the 20 amino acids

• N1, N2, …, N20 - the number of times each amino acid is observed in the sequence

Likelihood function:

• Let be n sequence(DNA or AA).

Assume for simplicity they are all same length, l.

• We want to learn the parameters of a phylogenetic tree that maximizes the likelihood.

• But wait: Should first specify a model.

• Our models will consist of a “regular” tree, where

in addition, edges are assigned substituion probabilities.

• For simplicity, assume our “DNA” has only two

states, say X and Y.

• If edge eis assigned probability pe, this means

that the probability of substitution (X Y)

across e is pe.

• Our models will consist of a “regular” tree, where

in addition, edges are assigned substituion probabilities.

• For simplicity, assume our “DNA” has only two

states, say X and Y.

• If edge eis assigned probability pe, this means

that the probability of substitution (X Y)

across e is pe.

• If edge eis assigned probability pe, this means

that the probability of more involved patterns of

substitution across e(e.g.XXYXYYXYXX)

is determined, and easily computed: pe2(1- pe)3

for this pattern.

• Q.: What if pattern on both sides is known, but pe is

not known?

• A.: Makes sense to seek pe that maximizes

probability of observation.

• So far, this is identical to coin toss example.

But a single edge is a fairly boring tree…

Now we don’t know the states at internal node(s), nor

the edge parameters pe1, pe2, pe3

YXYXX

XXYXY

pe2

pe1

pe3

?????

YYYYX

Two Ways to Go

1. Maximize over states of internal node(s)

2. Average over states of internal node(s)

In both cases, we maximize over edge parameters

YXYXX

XXYXY

pe2

pe1

pe3

?????

YYYYX

Two Ways to Go

In the first version (average, or sum over states of internal

nodes) we are looking for the “most likely” setting of tree edges.

This is called maximum likelihood (ML) inference of

phylogenetic trees.

ML is probably the inference method most widely (wildly )

used.

YXYXX

XXYXY

pe2

pe1

pe3

?????

YYYYX

Two Ways to Go

In the second version (maximize over states of internal nodes)

we are looking for the “most likely” ancestral states. This is

called ancestral maximum likelihood (AML).

In some sense AML is “between” MP (having ancestral states)

and ML (because the goal is still to maximize likelihood).

YXYXX

XXYXY

pe2

pe1

pe3

?????

YYYYX

or a break

.