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Primer on Probability. Sushmita Roy BMI/CS 576 Sushmita Roy [email protected] Sep 25 th , 2012. BMI/CS 576. Definition of probability.

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Primer on probability

Primer on Probability

Sushmita Roy

BMI/CS 576

Sushmita Roy

[email protected]

Sep 25th, 2012

BMI/CS 576

Definition of probability
Definition of probability

  • frequentist interpretation: the probability of an event from a random experiment is the proportion of the time events of same kind will occur in the long run, when the experiment is repeated

  • examples

    • the probability my flight to Chicago will be on time

    • the probability this ticket will win the lottery

    • the probability it will rain tomorrow

  • always a number in the interval [0,1]

    0 means “never occurs”

    1 means “always occurs”

Sample spaces
Sample spaces

  • sample space: a set of possible outcomes for some event

  • event: a subset of sample space

  • examples

    • flight to Chicago: {on time, late}

    • lottery: {ticket 1 wins, ticket 2 wins,…,ticket n wins}

    • weather tomorrow:

      {rain, not rain} or

      {sun, rain, snow} or

      {sun, clouds, rain, snow, sleet} or…

Random variables
Random variables

  • random variable: a function associating a value with an attribute of the outcome of an experiment

  • example

    • X represents the outcome of my flight to Chicago

    • we write the probability of my flight being on time as P(X = on-time)

    • or when it’s clear which variable we’re referring to, we may use the shorthand P(on-time)


  • uppercase letters and capitalized words denote random variables

  • lowercase letters and uncapitalized words denote values

  • we’ll denote a particular value for a variable as follows

  • we’ll also use the shorthand form

  • for Boolean random variables, we’ll use the shorthand

Probability distributions









Probability distributions

  • if X is a random variable, the function given by P(X = x)for each x is the probability distribution of X

  • requirements:

Joint distributions
Joint distributions

  • joint probability distribution: the function given by P(X = x, Y = y)

  • read “X equals xandY equals y”

  •  example

probability that it’s sunny

and my flight is on time

Marginal distributions
Marginal distributions

  • the marginal distribution of X is defined by

    “the distribution of X ignoring other variables”

  • this definition generalizes to more than two variables, e.g.

Marginal distribution e xample
Marginal distribution example

joint distribution

marginal distribution for X

Conditional distributions
Conditional distributions

  • the conditional distribution of Xgiven Y is defined as:

    “the distribution of X given that we know the value of Y”

Conditional distribution e xample
Conditional distribution example

conditional distribution for X


joint distribution


  • two random variables, X and Y, are independent if 

Independence example 1
Independence example #1

joint distribution

marginal distributions

Are X and Y independent here?


Independence example 2
Independence example #2

joint distribution

marginal distributions

Are X and Y independent here?


Conditional independence
Conditional independence

  • two random variables X and Y are conditionally independent given Z if 

    • “once you know the value of Z, knowing Y doesn’t tell you anything about X”

  • alternatively

Conditional independence e xample
Conditional independence example

Are Fever andHeadache independent?


Conditional independence e xample1
Conditional independence example

Are Fever and Vomitconditionally independent given Flu:


Chain rule of probability
Chain rule of probability

  • for two variables

  • for three variables

  • etc.

  • to see that this is true, note that

Bayes theorem
Bayes theorem

  • this theorem is extremely useful

  • there are many cases when it is hard to estimate P(x| y) directly, but it’s not too hard to estimate P(y| x) andP(x)

Bayes theorem e xample
Bayes theorem example

  • MDs usually aren’t good at estimating P(Disorder| Symptom)

  • they’re usually better at estimating P(Symptom| Disorder)

  • if we can estimate P(Fever| Flu) and P(Flu) we can use Bayes’ Theorem to do diagnosis

Expected values
Expected values

  • the expected value of a random variable that takes on numerical values is defined as:

    this is the same thing as the mean

  • we can also talk about the expected value of a function of a random variable

Expected value e xamples
Expected value examples

  • Suppose each lottery ticket costs $1 and the winning ticket pays out $100. The probability that a particular ticket is the winning ticket is 0.001.

The binomial d istribution
The binomial distribution

  • distribution over the number of successes in a fixed number n of independent trials (with same probability of success p in each)

  • e.g. the probability of x heads in ncoin flips






The multinomial d istribution
The multinomial distribution

  • k possible outcomes on each trial

  • probability pifor outcome xi in each trial

  • distribution over the number of occurrences xifor each outcome in a fixed number n of independent trials

  • e.g. with k=6 (a six-sided die) and n=30

vector of outcome


Statistics of alignment s cores
Statistics of alignment scores

Q: How do we assess whether an alignment provides good evidence for homology?

A: determine how likely it is that such an alignment score would result from chance.

What is “chance”?

  • real but non-homologous sequences

  • real sequences shuffled to preserve compositional properties

  • sequences generated randomly based upon a DNA/protein sequence model

Model for u nrelated s equences
Model forunrelatedsequences

  • we’ll assume that each position in the alignment is sampled randomly from some distribution of amino acids

  • let be the probability of amino acid a

  • the probability of an n-character alignment of x and y is given by

Model for r elated s equences
Model forrelatedsequences

  • we’ll assume that each pair of aligned amino acids evolved from a common ancestor

  • let be the probability that evolution gave rise to amino acid a in one sequence and b in another sequence

  • the probability of an alignment of x and y is given by

Probabilistic model of alignments

Probabilistic model of alignments

  • How can we decide which possibility (U or R) is more likely?

  • one principled way is to consider the relative likelihood of the two possibilities

Probabilistic model of alignments1
Probabilistic model of alignments

  • the score for an alignment is thus given by:

  • the substitution matrix score for the pair a, b should thus be given by:

Scores from random a lignments
Scores from random alignments

  • suppose we assume

    • sequence lengths m and n

    • a particular substitution matrix and amino-acid frequencies

  • and we consider generating random sequences of lengths m and n and finding the best alignment of these sequences

  • this will give us a distribution over alignment scores for random pairs of sequences

The extreme v alue d istribution
The extreme value distribution

  • but we’re picking thebest alignments, so we want to know what the distribution of max scores for alignments against a random set of sequences looks like

  • this is given by an extreme value distribution

Distribution of scores
Distribution of scores

  • the expected number of alignments, E, with score at least S is given by:

  • S is a given score threshold

  • m and n are the lengths of the sequences under consideration

  • K and are constants that can be calculated from

    • the substitution matrix

    • the frequencies of the individual amino acids

Statistics of alignment s cores1
Statistics of alignment scores

  • to generalize this to searching a database, have n represent the summed length of the sequences in the DB (adjusting for edge effects)

  • the NCBI BLAST server does just this

  • theory for gapped alignments not as well developed

  • computational experiments suggest this analysis holds for gapped alignments (but K and must be estimated from data)