1.3 Conditional Probability
Example 1.3-10
THIS
Example 1.3-11
THIS
Example 1.3-12
THIS
Example 1.3-6
THIS
Extending multiplication rule for conditional probabilities
THIS read the LHS then try to visualize or solve
Example 1.3-7
THIS you should learn that it is convenient to separate the event into two sections - normal and conditional probabilities - to calculate the intersection of them
The conditional probability of a sum of 3 given that a sum of 3 or 5 has occurred
probability that a sum of 3 is rolled before a sum of 5 is rolled can be thought of as
1. P(A|B) ≥ 0 2. P(B|B) = 1 3. if A₁, A₂, ... are mutually exclusive events, then P(A₁ ∪ A₂ ∪ ... A(k)|B) = P(A₁|B) + P(A₂|B) + ... + P(A(k)|B)
the three axioms for the conditional probability
Deduce P(A|B) from N(A) and N(B)
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Definition of the multiplication rule for the conditional probability
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Example 1.3-9 think of this example as an extension of multiplication rule for conditional probabilities
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P(A|B)
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Prove that 1. P(A|B) ≥ 0 2. P(B|B) = 1 hold
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