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PROBABILITY

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Uncertainty Let action A t = leave for airport t minutes before flight from Logan Airport Will A t get me there on time ? Problems : 1. Partial observability ( road state, other drivers ' plans, etc.). 2. Noisy sensors ( traffic reports ). 3. Uncertainty in action outcomes ( flat tire, etc.). 4. Immense complexity of modeling and predicting traffic.

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Uncertainty Let action A t = leave for airport t minutes before flight from Logan Airport Will A t get me there on time ? A purely logical approach either : 1) risks falsehood : “ A 120 will get me there on time,” or 2) leads to conclusions that are too weak for decision making : “ A 120 will get me there on time if there ' s no accident on I -90 and it doesn ' t rain and my tires remain intact, etc., etc.” ( A 1440 might reasonably be said to get me there on time but I ' d have to stay overnight in the airport … )

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Methods for handling uncertainty Default logic : Assume my car does not have a flat tire Assume A 120 works unless contradicted by evidence Issues : What assumptions are reasonable ? How to handle contradiction ? Probability Model agent ' s degree of belief Given the available evidence, A 120 will get me there on time with probability 0.4

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Probability Probabilistic assertions summarize effects of laziness : failure to enumerate exceptions, qualifications, etc. ignorance : lack of relevant facts, initial conditions, etc. Subjective probability : Probabilities relate propositions to agent ' s own state of knowledge e. g., P ( A 120 | no reported accidents ) = 0.6 These are not assertions about the world, but represent belief about the whether the assertion is true. Probabilities of propositions change with new evidence : e. g., P ( A 120 | no reported accidents, 5 a. m.) = 0.15

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Making decisions under uncertainty Suppose I believe the following : P ( A 135 gets me there on time |...) = 0.04 P ( A 180 gets me there on time |...) = 0.70 P ( A 240 gets me there on time |...) = 0.95 P ( A 1440 gets me there on time |...) = 0.9999 Which action to choose ? Depends on my preferences for missing flight vs. airport cuisine, etc. Utility theory is used to represent and infer preferences Decision theory = utility theory + probability theory

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Quick Question You go to the doctor and are tested for a disease. The test is 98% accurate if you have the disease. 3.6% of the population has the disease while 4% of the population tests positive. How likely is it you have the disease ?

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Syntax Basic element : random variable Boolean random variables e. g., Cavity ( do I have a cavity ?) Discrete random variables e. g., Weather is one of Continuous random variables represented by probability density functions ( PDFs ), valued [0,1]

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Syntax Elementary propositions constructed by assignment of a value to a random variable e. g., Weather = sunny, Cavity = false ( abbreviated as sunny or cavity ) Complex propositions formed from elementary propositions and standard logical connectives e. g., ( Weather = sunny Cavity = false ) e. g., ( sunny cavity )

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Syntax Atomic event : A complete specification of the state of the world. A complete assignment of values to variables. E. g., if the world consists of only two Boolean variables Cavity and Toothache, then there are 4 distinct atomic events : Cavity = false Toothache = false Cavity = false Toothache = true Cavity = true Toothache = false Cavity = true Toothache = true Atomic events are mutually exclusive and exhaustive

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Axioms of probability ( Kolmogorov ’ s Axioms ) For any propositions A, B 1. 0 ≤ P ( A ) ≤ 1 2. P ( true ) = 1 and P ( false ) = 0 3. P ( A B ) = P ( A ) + P ( B ) - P ( A B )

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Priors Prior or unconditional probabilities of propositions correspond to belief prior to arrival of any ( new ) evidence. e. g., P ( cavity ) = P ( Cavity = true )= 0.1 P ( Weather = sunny ) = 0.72 P ( cavity Weather = sunny ) = 0.072

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Distributions Probability distribution gives probabilities of all possible values of the random variable. Weather is one of P ( Weather ) = ( Normalized, i. e., sums to 1. Also note the bold font..)

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Joint Probability The entire table sums to 1.

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Inference by Enumeration

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Conditional Probability

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Inference by Enumeration

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Conditional Probability

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Absolute Independence

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Absolute independence powerful but rare For n independent biased coins, O (2 n ) →O ( n ) 2*2*2*4=32 entries 2*2*2+4=12 entries

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Conditional independence If I have a cavity, the probability that the probe catches in it doesn ' t depend on whether I have a toothache : P ( catch | toothache, cavity ) = P ( catch | cavity ) The same independence holds if I haven ' t got a cavity : P ( catch | toothache, cavity ) = P ( catch | cavity ) Catch is conditionally independent of Toothache given Cavity : P ( Catch | Toothache, Cavity ) = P ( Catch | Cavity ) Equivalent statements : P ( Toothache | Catch, Cavity ) = P ( Toothache | Cavity ) P ( Toothache, Catch | Cavity ) = P ( Toothache | Cavity ) P ( Catch | Cavity )

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Bayes ' Rule

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Much easier to calculate/estimate than the part on the left!

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Bayes Rule : Example Note: probability of meningitis still very small!

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Bayes ' Rule and conditional independence

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Naïve Bayes Model

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Summary Probability is a formalism for uncertain knowledge Basic probability statements include prior probabilities and conditional probabilities The full joint probability distribution specifies probability of each complete assignment of values to variables. Usually too large to create and use in its explicit form. Absolute independence between subsets of random variables allows the full joint distribution to be factored into smaller joint distributions. Absolute independent rarely occurs in practice. Bayes ’ rule allows unknown probabilities to be computed from known conditional probabilities. Applying Bayes ’ rule with many variables runs into the same scaling problem as above. Conditional independence, brought about by direct causal relationships in the domain, may allow the full joint distribution to be factored into smaller, conditional distributions. Naïve Bayes model assumes conditional independence of all effect variables.

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CPSC 322, Lecture 24Slide 1 Reasoning under Uncertainty: Intro to Probability Computer Science cpsc322, Lecture 24 (Textbook Chpt 6.1, 6.1.1) March, 15,

CPSC 322, Lecture 24Slide 1 Reasoning under Uncertainty: Intro to Probability Computer Science cpsc322, Lecture 24 (Textbook Chpt 6.1, 6.1.1) March, 15,

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