Probability Review Additions
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The document discusses key concepts in probability theory including random variables, sample spaces, events, atomic events, laws of probability, conditional probabilities, independence, multivariate distributions, and Bayes' theorem. Random variables can be discrete or continuous. A sample space represents all possible outcomes and events are subsets of the sample space. The probability of the sample space is 1 and probabilities of events range from 0 to 1. Conditional probabilities and independence are discussed. Bayes' theorem provides a way to calculate conditional probabilities.
Probability Review Additions
- 1. Quick Review Probability Theory
- 2. Reasoning and Decision Making Under Uncertainty Uncertainty, Rules of Probability Bayes’ Theorem
- 3. Causes of not knowing things precisely Uncertainty Vagueness Incompleteness Bayesian Technology Fuzzy Sets and Fuzzy Logic Default Logic and Reasoning Belief Networks If Bird(X) THEN Fly(X) Reasoning with concepts that do not have a clearly defined boundary; e.g. old, long street, very odl…”
- 4. Random Variable Definition: A variable that can take on several values, each value having a probability of occurrence. There are two types of random variables: Discrete. Take on a countable number of values. Continuous. Take on a range of values.
- 5. The Sample Space The space of all possible outcomes of a given process or situation is called the sample space S. S red & small blue & small red & large blue & large
- 6. An Event An event A is a subset of the sample space. S red & small blue & small red & large blue & large A
- 7. Atomic Event An atomic event is a single point in S. Properties: Atomic events are mutually exclusive The set of all atomic events is exhaustive A proposition is the disjunction of the atomic events it covers.
- 8. The Laws of Probability The probability of the sample space S is 1, P(S) = 1 The probability of any event A is such that 0 <= P(A) <= 1. Law of Addition If A and B are mutually exclusive events, then the probability that either one of them will occur is the sum of the individual probabilities: P(A or B) = P(A) + P(B)
- 9. The Laws of Probability If A and B are not mutually exclusive: P(A or B) = P(A) + P(B) – P(A and B) A B
- 10. Conditional Probabilities and P(A,B) Given that A and B are events in sample space S, and P(B) is different of 0, then the conditional probability of A given B is P(A|B) = P(A,B) / P(B) If A and B are independent then P(A,B)=P(A)*P(B) P(A|B)=P(A) In general: min(P(A),P(B) P(A)*P(B) max(0,1-P(A)-P(B)) For example, if P(A)=0.7 and P(B)=0.5 then P(A,B) has to be between 0.2 and 0.5, but not necessarily be 0.35.
- 11. The Laws of Probability Law of Multiplication What is the probability that both A and B occur together? P(A and B) = P(A) P(B|A) where P(B|A) is the probability of B conditioned on A.
- 12. The Laws of Probability If A and B are statistically independent: P(B|A) = P(B) and then P(A and B) = P(A) P(B)
- 13. Independence on Two Variables P(A,B|C) = P(A|C) P(B|A,C) If A and B are conditionally independent: P(A|B,C) = P(A|C) and P(B|A,C) = P(B|C)
- 14. Multivariate Joint Distributions P(x,y) = P( X = x and Y = y). P’(x) = Prob( X = x) = ∑ y P(x,y) It is called the marginal distribution of X The same can be done on Y to define the marginal distribution of Y, P”(y). If X and Y are independent then P(x,y) = P’(x) P”(y)
- 15. Bayes’ Theorem P(A,B) = P(A|B) P(B) P(B,A) = P(B|A) P(A) The theorem: P(B|A) = P(A|B)*P(B) / P(A) Example: P(Disease|Symptom)= P(Symptom|Disease)*P(Disease) / P(Symptom)