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22 Cards in this Set
- Front
- Back
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Axiom 1 |
Certain event has probability 1
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Axiom 6 |
If A is contained in B , p(A) less than P(B)
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Conditional probability |
P(A|B) = P(AnB)/P(B) Probability of A given B P(B) must be greater than zero |
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Axiom 2 |
All probabilities are greater than 0 |
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Axiom 3 |
If A and B are disjoint events therefore AnB = 0, then P(AUB) = p(A) + p(B) |
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Axiom 4 |
Complement rule P(S - A) = 1 - P(A) for an event A |
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Axiom 7 |
General rule P(AUB) = P(A) + P(B) - P(AnB) for given events A and B |
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Axiom 5 |
P(0) = 0 |
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Is 'and' intersection or union? |
Intersection |
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Axiom 1 (Conditional probability) |
P(A|A) = P(AnA):P(A) = P(A):P(A) = 1 |
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Axiom 2 (Conditional probability) |
P(B|A) >=0 bc P(AnB) >=0 and P(A)>0 |
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Axiom 3 (Conditional probability) |
If BnC = null but AuC and BuC exist it means (BnA)n(CnA)=null Then P[(BnA)u(CnA)] = P(BnA)+P(CnA) P(BuC|A)= P(BnA)+P(CnA):P(A) = P(B|A) + P(C|A) |
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What is Bayes' rule? |
P(B|A) = P(A|B)*P(B):P(A) |
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If events A and B are independent , what does this mean ? |
Event A not affected by occurrence of event B and vice versa. |
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What is P(B|A) for an independent event ? |
P(B) |
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What is P(AnB) for an independent event ? |
P(A)P(B) |
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What is P(AnB) from conditional probabilities ? |
P(A|B)P(B) |
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For mutually exclusive events , what is P(AuB) ? |
P(A) + P(B) |
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Difference between probability outcome and event ?
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Outcome is what you get in sample space.
Event is probability of subset of sample space |
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Types of discrete distribution?
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1. Bernoulli distribution
2. Geometric distribution 3. Binomial distribution 4. Poisson distribution |
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Types of continuous distribution?
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1. Exponential distribution
2. Normal distribution |
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What are possibilities in Bernoulli trial?
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1 and 0
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