Probability Notes Math 309. Sample spaces, events, axioms Math 309 Chapter 1.
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Transcript of Probability Notes Math 309. Sample spaces, events, axioms Math 309 Chapter 1.
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Probability Notes
Math 309
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Some Definitions
• Experiment - means of making an observation
• Sample Space (S) - set of all outcomes of an experiment listed in a mutually exclusive and exhaustive manner
• Event - subset of a sample space
• Simple Event - an event which can only happen in one way; )
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Since events are sets, we need to understand the basic set operations
• Intersection (AB) - everything in A and B
• Union (A B) - everything in A or B or both
• Complement (AC ) - everything not in A
• Difference A – B = A BC – everything in A that is not in B
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• You should be able to sketch Venn diagrams to describe the intersections, unions, & complements of sets.
• Note that these set operations obey the commutative, associative, and distributive laws
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DeMorgan’s Laws
• (A B)C = (AC BC)
• (A B)C = (AC BC)
• Convince yourself that these are reasonable with Venn diagrams!
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Another definition -
A and B are mutually exclusive iff
A B =
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Axioms of Probability(these are FACT, no proof needed!)
Let A represent an event, S the sample space,
• P(S) = 1• For pairwise mutually exclusive events, the
probability of their union is the sum of their respective probabilities, i.e.
P(A1A2 . . . An . . .) =
P(A1)+P(A2)+ . . . +P(An) + . . .
0 ( ) 1P A
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From Axiom 3, it can be shown that:(Prop. 1*)
• Let {A1, A2, . . . ,An} be a mutually exclusive set of events. Then
P(A1A2 . . . An) = P(A1) + P(A2) + . . . + P(An)
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• Let A and B be mutually exclusive, our last theorem with n = 2 gives:
P(A B) = P(A) +P(B)
•
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More Theorems (Propositions)
Let A and B be any two events.
• Prop. 4.1 - P(AC ) = 1 - P(A)
• Prop. 4.2 - If A is a subset of B, then P(A) <= P(B)
• Prop. 4.3 - P(A B) = P(A) + P(B) - P(A B)
• Prop. 2* - P(A) = P(A B) + P(A BC)
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Unions get complicated if events are not mutually exclusive!
P(A B C) = P(A) + P(B) + P(C) - P(A B) - P(A C) - P(B C) + P(A B C)
A C
B
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Sample Spaces with Equally Likely Outcomes
In an experiment where all sample points are equally likely, one can find the probability of an event by counting two sets.
# of sample points in P(A)
# of sample points in S
A
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Combinatorial Methods
Math 309
Chapter 1
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Combinatorics
• Basic Principle of Counting– (a.k.a. Multiplication Principle)
• Permutations– Permutations with indistinguishable objects
• Combinations
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Basic Counting Principle
• If experiment 1 has m outcomes and experiment 2 has n outcomes, then there are mn outcomes for both experiments.
• The principle can be generalized for r experiments. The number of outcomes of r experiments is the product of the number of outcomes of each experiment.
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• We define experiment as a means of making an observation (e.g. flip a coin, choose a color).
• Each experiment could be making a choice from a different set.
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Permutations
• # of arrangements of one set, order matters
• application of the basic counting principle where we return to the same set for the next selection
• P(n,r) = n!/(n-r)!
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Permutations with Indistinguishable Objects
• Order the objects as if they were distinguishable
• Then “divide out” those arrangements that look identical.
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Combinations
• the number of selections, order doesn’t matter– C(n,r) = n!/[(n-r)!r!]
• the number of arrangements can be counted by selecting the objects and then ordering them– i.e. P(n,r) = C(n,r)*r!
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Observations about Combinations
• C(n, r) = C(n, n-r)
• C(n, n) = C(n, 0) = 1
• C(n, 1) = n = C(n, n-1)
• C(n, 2) = n(n-1)/2
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Combining Counting Techniques
• If we are careful with language,
– when we say “AND”, we multiply– “AND” multiplication intersection
– when we say “OR”, we add– “OR” addition union
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Conditional Probability and Independence
Math 309
Chapter 3
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Conditional Probability P(A|B)
• P(A|B) is read, “the probability of A given B”
• B is known to occur.
• P(A|B) = P(A B) / P(B), if P(B) > 0
• i.e. the conditional probability is the probability that both occur divided by what is given occurs
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The multiplication rule and intersectionmultiply
• P(A B) = P(A)*P(B|A) • = P(B)*P(A|B)• (Note that this is an algebraic manipulation of the formula for conditional probability.)
• Intersections get more complicated when there are more events, e.g.
• P(ABCD)• = P(A)* P(B|A)*P(C|AB)*P(D|A BC)
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Independent Events
• A and B are independent if any of the following are true:– P(AB) = P(A)*P(B)– P(A|B) = P(A)– P(B|A) = P(B)
• You need to check probabilities to determine if events are independent.
• If A, B, C, & D are pairwise independent,– P (AB C D) = P(A)*P(B)*P(C)*P(D)