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Basic Probability. Introduction. Our formal study of probability will base on Set theory Axiomatic approach (base for all our further studies of probability) We will solve a class of probability problems via counting methods Determining the probability of obtaining a royal flush in poker
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Introduction • Our formal study of probability will base on • Set theory • Axiomatic approach (base for all our further studies of probability) • We will solve a class of probability problems via counting methods • Determining the probability of obtaining a royal flush in poker • Obtaining a defective item from a batch of mostly good items.
Review of Set Theory • The set A can be defied either by the • enumeration method • description method • Each object is called an elementand it is distinct. • Set are equivalent. • Set are equivalent. • Sets are said to be equal if they contain the same elements. • If and then
Review of Set Theory • An element of x of a set is denoted and is read “x is contained in A”. • If then • Emptyset or null set • If the instructor in the class does not give out any grades “A” then the set of students receiving an “A” is . • Universal set • If the instructor is an easy grader and give out all “A”, then , where S is the set of all students enrolled in the class.
Review of Set Theory • Example 1. Set concepts • Consider the set of all outcomes of a tossed die. This is • The numbers 1,2,3,4,5,6 are its elements, which are distinct. • The set of integers numbers from 1 to 6 or is equal to A. • The set A is also the universal set since it contains all the outcomes. • The set is called a subset of A. • A simpleset is a set containing a single element,
Review of Set Theory • Element versus simple set • Sometimes elements in a set can be added, as, for example 2 + 3 = 5, but it makes no sense to add sets as in {2} + {3} = {5}; • More formally, a set B is defined as a subset of a set A if every element in B is also an element of A. It is denoted as . • We can say that if and . • New sets may be derived from other sets. • If , then is a subset of S. • The complement of A, denoted by or by is the set of elements in S but not in A. This is . • Two sets can be combined together or intersected.
Review of Set Theory • Two sets can be combined together to from a new set. If Then the union of A and B, denoted by is the set of elements that belongs to A or B or both A and B. Hence, . • The union of multiple sets is denoted by • The intersection of sets A and B, denoted by or by is defined as the set of elements that belong to both A and B. • Hence, for the sets above. • The intersection of multiple sets is denoted by • The difference between sets, denoted by is the set of elements in A but not in B. Hence, .
Venn diagrams • A Venn diagram is useful for visualizing set operations. • The darkly shaded regions are the sets described. • The dashed portions are not included in the sets
Venn diagrams • Example: One may inquire whether the following is true • Applying Venn diagram it is easy to see • To formally prove, let and prove that • a. • b. • a. Assume that then by def. but . Hence, . Since , . Hence, and since this is true for every we have that • b. Try to prove yourself.
Set operations: Example • Given the four sets: Find unions, , and , intersections , and , and complements: ,, and
Algebra of sets • 1. • 2. • 3. • 4. • 5. • If two sets A and B have no elements in common, they are said to be disjointi.e. . • If the sets contain between them all the elements of S, then the sets are said to partition the universe. • Mutually disjoint sets for all are said to partition the universe if .
Algebra of sets • Example: the set of students enrolled in the probability class is defined as the universe is partitioned by • Some useful algebraic rules are • 1. Commutative properties • 2. Associative properties • 3. Distributive properties
Algebra of sets • 4. De Morgan’s law. • In either case we can perform the conversion by the following set of rules: • Change the unions to intersections and intersection to unions • Complement each set • Complement the overall expression Unions to intersections Intersections to unions
Algebra of Sets: Examples • Example 1 • Verify by using the example sets • when • Example 2 • Demonstrate for three sets
Size of sets • Discrete sets • The set {2,4,6} is a finite set. • The set {2,4,6,…} is an infinite set, but countably infinite. (we can pair up each element in the set with an element in the set of natural numbers). • Continuous sets • The set is infinite. • Examples • finite set – discrete • countably infinite set – discrete • infinite set – continuous
Set definitions: Examples • Example 1. Identify the set • A = {1,3,5,7} • B = {1,2,3} • C = {0.5 < c ≤ 8.5} • D = {0} • E = {2,4,6,8,10,12,14} • F = {-0.5 < f ≤ 12.0} • Example 2 • The universal set of a rolling die is S = {1,2,3,4,5,6} . The person wins if the number comes up odd A = {1,3,5}, another person wins when the number is 4 or less A = {1,2,3,4}. • Both A and B are subsets of S. • For any universal set with N elements, there are 2N possible subsets of S. • So, there are 2N= 64 ways one can define “winning” with one die.
Assigning and Determining Probabilities • The concept of sets and operations on sets provide an ideal description for probabilistic model and the means for determining the probabilities associated with the model. • S = {1,2,3,4,5,6} is a universal set for a fair die. • S is termed sample space and its elements are the outcomes. • We are interested in particular outcomes. • Even number outcome Eeven = {2,4,6}. • Simplest events with one elements E1= {1}, E2= {2}… • Other events are S – certain event and 0={} – impossible event. • Disjoint sets {1, 2} and {3, 4} are said to be mutually exclusive, hence events are mutually exclusive. An event occurs if the outcome is an element of the defining set of that event
Assigning and Determining Probabilities • What is the probability P[Eeven]that tossed die will produce an even outcome? Intuitively, there are 3 chances out of 6 so P[Eeven] = ½; • Note, that P is a probability function that assigns a number between 0 and 1 sets. • Examples • For coin toss there are two events head H or T, all events are E1 = {H}, E2 = {T}, E3 = S and E4 = 0; • For a die toss all the events are E0= 0, E1 = {1},…,{6}, E12 = {1,2},…,E56 = {5,6},…,E123456 = {1,2,3,4,5,6}. There are total 64 events. • In general, if the sample space has N simple events, the total number of events is 2N.
Assigning and Determining Probabilities: Axiomatic approach • Axiom 1 P[E] ≥ 0 for every event E • Axiom2 P[S] = 1, • Axiom 3P[E U F]= P[E] + P[F] for E and F mutually exclusive. • Axiom 3’ for all Ei’s mutually exclusive. • Axiom 4 Not a trivial transition.
Assigning and Determining Probabilities: Example • A number x is obtained by spinning the pointer on a “fair” wheel of chance that is labeled from 0 to 100 points. • The sample set is S = {s: 0 < s ≤ 100}. • The probability of the pointer falling between x2 ≥ x1 should be (x2 – x1) / 100 • Axiom 1 is satisfied for all x1 and x2 : 0 ≤ (x2 - x1)/100 • Axiom 2 is satisfied when x2 = 100 and x1 = 0 • If break wheels periphery into N segments Then and for any N. If (xn– xn-1) 0 then P(An) P(xn) is the probability of the pointer falling exactly on the point xn. P(xn) = 0, because N 0. 0 75 25 Axiom 3 50
Die toss example • Determine the probability that the outcome of a fair die toss is even Eeven= {2,4,6}. • Defining Eias the simple event {i} we note that • And from Axiom 2 we must have • But since each Eiis a simple event and by definition the simple events are mutually exclusive, we have from axiom 3’ that • It is assumed that outcomes are equally likely P[E1] = P[E2] = … = P[E6] = p. • Hence, P[Ei] = 1/6 for all i. We can determine P[Eeven] since Eeven = E2U E4U E6 by applying Axiom 3’ again P[Eeven] = P[E2U E4U E6] = P[E2] + P[E4]+ P[E6] = 1/6 + 1/6 + 1/6 = 1/2
Assigning and Determining Probabilities • In, general P need not to be the same (weighted die). • Letting P[{si}] be the probability of the ith simple event we have that • To simplify the notation instead P[{1}] we will use P[1].
Countably infinite sample • A habitually tardy person arrives at the theater later by si minutes, where si = i, i = 1,2,3,… • If P[si] = (1/2)i what is the probability that he will be more than 1 minute late? • The event is E = {2,3,4,…}. Then we have • Using formula for the sum of a geometric progression • we have that
Mathematical model of Experiment A real experiment is defined mathematically by three things: • Assignment of a sample space; • Definition of events of interest; • Making probability assignments to the events such that axioms are satisfied. Example: Observe the sum of the numbers showing up when two dice are thrown. • Sample space consists of 62 = 36 points • We are interested in • A = {sum ==7}, B = {8 < sum ≤ 11}, and C = {10 < sum } • Eij = {sum of outcomes (i,j) = i + j}
Properties of the Probability function • Based on four axioms we may derive useful properties Property 1. Probability of complement event P[Ec] = 1 -P[E] Proof: By definition E U Ec= S, and sets E and Ecare mutually exclusive. Hence From which the property follows. Property 2. Probability of impossible event is 0. Proof: Since we have but if P[E] = 0, does not mean E is impossible.
Practice problems • 1. • 2.
