Probability is the study of randomness. Random Experiment: Ex: Flip a coin, roll a die, results of a Covid test, ... - It is random because the outcome is uncertain - Can list all possible outcomes Defun 1: The sample space associated with an experiment, Omega, is the set of possible outcomes of an experiment. Ex: Flip a coin: Omega = {heads, tails} | {heads, tails, sides} Roll a seven-sided die = {1..7} The experiment, rigorously, is the # of dots on the top face. Cardinality of set is number of elements of set. Cardinality of empty set is 0. (why is this a convention and not a self-evident fact?) Card S = #S = cardinality of S Flip a coin until first tail, and stop. Count # of flips. Omega = {1,2,3,...} = Natural numbers. (Sometimes also {0,1,2,...}) Card N = +\infty Suppose you flip a coin and want to assess the odds of getting tails on a flip. For any given coin, we don't know. How can we figure out? One possibility: Keep flipping coin. # of tails / # of flips is intuitively close to the probability of getting tails. T(n) = # of tails / # of flips. Intuitively, 0 <= Omega(n) = T(n)/n <= 1. For n large, Omega(n) ~ probability of tails on a single flip. The law of large numbers tells us that T(n)/n will converge as n -> +\infty, to p---the probabiity of getting tails on a single flip. "Wisdom of Crowds" somehow obeys the law of large numbers. This is the "frequentist" approach for probability theory "Subjective approach" to probability theory - Better at answering question like "what is the odds that Dow Jones grows by 113 points by EOD?" d) Pick a point at random in the unit square and observe its coordinates What is the probability space? Omega = [0,1] x [0,1] = [0,1]^2 = {(x,y) : 0<=x<=1 : 0 <=y<=1} - Could also have excluded the boundary, making it (0,1)^2 #Omega = +\infty Assuming a uniform distribution: The odds of choosing a point in the bottom left corner triangle is 1/8. The odds of choosing a circle with a radius of 1/4 is pi/16. Generally, area/total = odds. Point area = 0, so odds of choosing it is 0. How to formalize the uniform distribution?