1. Lecture 2, CS5671 Review of Probability & Statistics Living with (Im)probability Probability Theory versus Calculus Permutations & Combinations Independence and Conditional probabilities Random variables Probability mass/density functions Expectation/Mean Variance/Standard deviation Probability distributions

2. Lecture 2, CS5672 (Im)Probability a blessing in disguise Imagine a deterministic world –Everything pre-ordained; “Free-will” a myth -Everything is known about the past, present and future Uncertainty = Spice of Life –“What would you like to be when you grow up?” –Chances of winning an Olympic medal, the lottery –Odds of Marrying Elizabeth Taylor !? –Foundation of Quantum Mechanics –Multiple histories of the Universe –“The only thing you can be certain of in life is uncertainty” “The most important questions of life are, for the most part, really only problems of probability” – Pierre Simon One of the reasons for this course!

3. Lecture 2, CS5673 Probability Theory versus Calculus Theory –Mapping real world to a mathematical model –Differing interpretations of probability possible Frequentist –Probability = Countable frequencies –Idealized (“All men are created equal”) –Tacit and unspecified underlying assumptions Subjective –Measure of belief –Explicit representation of all aspects of uncertainty –More general case, includes the frequentist view as a special case –More recent trend –Focus of this course (Bayesian framework) Calculus –Manipulation of the mathematical representation –Consensus on approach exists, same for all “Probability Theories”

4. Lecture 2, CS5674 Permutations and Combinations Order important in permutation –Biological sequences are permutations of respective alphabet –What is the total possible number of proteins? Order irrelevant in combination (set or multiset) -Set of genes of a species -How many different instances of Homo sapiens can be created?

5. Lecture 2, CS5675 Independence and Conditional Probability Independence (Between two events/distributions) –Probability of getting an A in this course and having purple hair (Independent or Dependent?) –Probability of a student attending this class, and being registered for this class (Independent or Dependent?) Conditional Probability (Calculation based on prior event) –Given that a student is attending this class, what is the probability of having registered? –Given that a student is attending this class, what is the probability that he is smart? IndependentNot independent

6. Lecture 2, CS5676 Random variables “Everyone/Everything is a stochastic statistic” Variable whose value in a specific trial/sample cannot be exactly predicted Discrete –Non-zero probabilities exist for the variable to take on each of a set of values –How many A’s will a student get? (Variable = Letter grade) Continuous –Non-zero probabilities exist for the variable to lie within a set of ranges of values –What is the probability that the Instructor’s weight is 135 pounds?

7. Lecture 2, CS5677 Joint random variables Particular values of multiple variables being observed together –Probability of getting a 7’0” center who has a free throw percentage greater than 90% Marginal probability of one of the joint variables –Consider all possible values of the remaining values (integration for the continuous case) –Probability of getting a 7’0” center. Period. (No matter what the free throw shooting percentage is) –Probability of spotting a car with mileage better than 30 mpg = [|ToyotaModels| + |HondaModels|…..]/|All Cars|

8. Lecture 2, CS5678 Probability mass/density functions Probability mass function –Function for computing probability for discrete variables Probability density function –Function for computing probability for continuous variables Cumulative distribution function –For either discrete or continuous variables, compute probability for all values greater/less than a specified limit The Probability Mass and Cumulative Distribution Functions have values ranging from 0 to 1, while the Probability Density Function has only a lower bound of 1

9. Lecture 2, CS5679 Expectation/Mean Expectation –The general case of “average” Not restricted to counts Any experimental result that is numeric –What can one expect on average? –Captures the central tendency –Linear operator Additive –Example: Expectation of Heads in a single toss of a coin = 0.5 Expectation of 2 heads in 2 tosses of a coin = 0.25 Expectation of the Instructor winning the Nobel prize = 10 -10000000000…… Average price of an airline ticket to Hawaii –Frequently used to establish the null case in experiments (Mind Reading/ Gender prediction scams!)

10. Lecture 2, CS56710 Variance/Standard Deviation Captures inconsistency/irreproducibility/error –“Always scores 20 points per game” versus “Anywhere between 0 and 40 points per game” Measure of dispersion Squaring in expression for variance ensures capturing symmetrical dispersion Standard deviation –Same unit as variable –Useful for normalized comparisons between experiments –Useful to estimating significance of observed values

11. Lecture 2, CS56711 Probability distributions Not just the Expectation, but the whole shebang –Probabilities of all possible values a variable can take Finding the probability distribution for a variable is important If probability distribution is known –Expectation can be calculated –Variance/Standard deviation can be calculated –Unusualness/Significance of an experimental finding can be verified –Data can be assigned to different probability distributions

12. Lecture 2, CS56712 Types of Probability distributions Uniform –Everything equally possible –Probability of an event inversely proportional to range of possibilities –Probability that a student entering Twin Oaks lives on a specific floor –“Truly random” Roulette wheel –Coin Toss

13. Lecture 2, CS56713 Types of Probability distributions Bernoulli –Variable can take value A xor B –“She has her mother’s complexion!” “No, she doesn’t!” –Odds of getting a head in a single coin toss Binomial –Result of multiple Bernoulli trials –Odds of m of n children inheriting their mother’s complexion –Odds of getting m heads in n coin tosses Multinomial –“Generalized binomial”: More than two outcomes –Number distribution in multiple rolls of a die –Residue composition of DNA or protein sequence

14. Lecture 2, CS56714 Types of Probability distributions Poisson –Number of events occurring in an interval, given an average rate –Approximation of a Binomial distribution with large number of trials and low probability of success –Number of traffic accidents Gaussian –Classical bell shape/also called Normal distribution –Distribution of estimates of Mean of a variable, irrespective of the underlying distribution of the variable per se –Unit normalized distribution allows useful interpretation Extreme value distribution –Skewed –Fashion/Toy industries thrive on this! “Everyone has a Star Wars light saber. I’ve got to have one!!”

15. Lecture 2, CS56715 Types of Probability distributions Student t distribution –Bell shaped, but with wider flanges –Distribution of estimate of mean, normalized by estimate of standard deviation (“error compounded by error”) Chi-squared distribution –Distribution of sums of squares of multiple random variables, each having a normal distribution –Error in hitting a baseball = Sum(Error in seeing ball, error in timing hit, error in centering hit..)

16. Lecture 2, CS56716 Types of Probability distributions F distribution –Distribution of ratio of a pair of normalized Chi-square variables –“Distribution of distributions” –Compares variances Multi-variate normal distribution –Generalization of normal distribution to multiple variables –Distribution of a vector of random variables

17. Lecture 2, CS56717 Types of Probability distributions