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Axioms of Probability: Understanding Sample Spaces, Events, and Probability Theory - Prof., Study notes of Probability and Statistics

This chapter from a probability textbook introduces the concepts of sample spaces, events, and the axioms of probability. It covers the motivating example of calculating the probability of being dealt a full house in poker, the definitions of sample space and events, and the operations of union, intersection, and complement. The text also discusses the commutative, associative, distributive laws, and demorgan's laws. The chapter concludes with the relative frequency definition of probability and some simple propositions.

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2009/2010

Uploaded on 02/24/2010

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Chap 2: Axioms of Probability
Motivating Example:
EX: A 5-card poker hand is said to be a full house if it consists of 3 cards of the same
denomination and 2 cards of the same denomination. (That is, a full house is three of a kind
plus a pair.) What is the probability that one is dealt a full house?
2.2 Sample Space and Events
A pair of concepts
Sample space: the set of all possible outcomes of an experiment. Denoted by S.
Event: any subset of the sample space is know as an event. Denoted by E.
EX: First row a die, then toss a coin. What is the sample space?
Example on textbook. (bkpg 22-23)
Union, Intersection, and Complement
1. union: E U F
2. Intersection: EF
3. Complement: Ec
Venn diagram:
Example: The following table summarizes visitors to a local amusement park:
One visitor from this group is selected at random:
1) Define the event A as “the visitor purchased an all-day pass”
2) Define the event B as “the visitor selected purchased a half-day pass”
3) Define the event C as “the visitor selected is female”
Describe the relationship and draw Venn diagram.
Rules for operation of forming Unions, Intersections, and Complements:
1. Commutative laws: E U F = F U E, E F = F E
2. Associative laws: ( E U F ) U G = E U ( F U G ), ( E F ) ∩ G = E ∩ ( F G )
3. Distributive laws: ( E U F ) ∩ G = ( EG ) U ( F G )
4. DeMorgan’s laws:
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All-Day Half-Day
Pass Pass Total
Male 1200 800 2000
Female 900 700 1600
Total 2100 1500 3600
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Chap 2: Axioms of Probability

Motivating Example: EX: A 5-card poker hand is said to be a full house if it consists of 3 cards of the same denomination and 2 cards of the same denomination. (That is, a full house is three of a kind plus a pair.) What is the probability that one is dealt a full house? 2.2 Sample Space and Events A pair of concepts Sample space: the set of all possible outcomes of an experiment. Denoted by S. Event: any subset of the sample space is know as an event. Denoted by E. EX: First row a die, then toss a coin. What is the sample space? Example on textbook. (bkpg 22-23) Union, Intersection, and Complement

1. union: E U F 2. Intersection: EF 3. Complement: E c Venn diagram: Example: The following table summarizes visitors to a local amusement park: One visitor from this group is selected at random:

  1. Define the event A as “the visitor purchased an all-day pass”
  2. Define the event B as “the visitor selected purchased a half-day pass”
  3. Define the event C as “the visitor selected is female” Describe the relationship and draw Venn diagram. Rules for operation of forming Unions, Intersections, and Complements: 1. Commutative laws: E U F = F U E , EF = FE 2. Associative laws: ( E U F ) U G = E U ( F U G ), ( EF ) ∩ G = E ∩ ( FG ) 3. Distributive laws: ( E U F ) ∩ G = ( EG ) U ( FG )

4. DeMorgan’s laws:  

n i c i n^ c i E (^) i E  1  1 ^      

n i c i n^ c i E i E  1  1 ^       All-Day Half-Day Pass Pass Total Male 1200 800 2000 Female 900 700 1600 Total (^) 2100 1500 3600

(We will come back to these rules when we need them later in this chapter.) 2.3 Axioms of Probability Relative frequency definition of probability: P( E ) = n n E n

lim  Here, an experiment with sample space S , is repeatedly performed under same conditions, E is an event in the sample space, n(E) is the number of times in first n repetitions of the experiment that the event E occurs. Axiom 1~3 (bkpg 27) Examples on textbook. (bkpg 28) 2.4 Some Simple Propositions P(Ec) = 1- P(E)

If E ^ F , then P(E) ≤ P(F)

P(E U F) = P(E) + P(F) - P(EF) (General Addition Rule) If E and F are mutually exclusive, then P(E U F) = P(E) + P(F). (Special Addition Rule, i.e. DeMorgan’s laws) Example: A consumer is selected at random. The probability the consumer has tried a snack food (F) is 0.5, tried a new soft drink (D) is 0.6, and tried both the snack food and the soft drink is 0.2. Find the following probabilities: 1). P( did not tried snack food ) 2). P( tried neither snack food nor soft drink ) 3). P( tried snack food but not soft drink ) 4). P( tried snack food or soft drink ) 2.5 Sample Spaces Having Equally Likely Outcomes Now we consider the situation: all outcomes in the sample space are equally likely to occur. Say, the sample space is S , and E is the event in the sample space, then number of outcomes in S number of outcomes in E P ( E ) Examples on textbook (bkpg 34, 35, 37, 39) 2.7 Probability as A Measure of Belief Read the textbook bkpg 48. ================== Chapter Summary: read the summary on the textbook.