probability reasoning
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The document discusses uncertainty and probabilistic reasoning. It begins by defining uncertainty as situations where the truth or falsity of predicates is unknown. This requires probabilistic reasoning to represent uncertain knowledge. Probabilistic reasoning is necessary for domains where complete and consistent models cannot be created, like the real world which contains true uncertainty. Bayes' rule is then introduced as a mathematical formula for determining conditional probabilities of events given prior knowledge. Examples are provided to demonstrate calculating conditional probabilities. The document contrasts rule-based systems with machine learning approaches for probabilistic reasoning.
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probability reasoning
- 2. contents 01 UNCERTAINITY CAUSE OF UNCERTENTAINTY 02 03 PROBABILITY REASONING NEED OF PROBABLITY REASONING 04 CONDITIONAL PROBABILITY 05 RULE BASED IN AI AND MACHINE LEARNING RULES OF PROBABILITY REASONING 06 REASONING TYPES
- 3. 01 UNCERTAINITY CAUSES OF UNCERTAINITY
- 4. Uncertainty: • Till now, we have learned knowledge representation using first-order logic and propositional logic with certainty, which means we were sure about the predicates. With this knowledge representation, we might write A→B, which means if A is true then B is true, but consider a situation where we are not sure about whether A is true or not then we cannot express this statement, this situation is called uncertainty. • So to represent uncertain knowledge, where we are not sure about the predicates, we need uncertain reasoning or probabilistic reasoning.
- 5. 03 Equipment fault . 04 Temperature variation 01 02 Information occurred from unreliable sources. . Experimental Errors YOUR TITLE 01 YOUR TITLE 05 Climate change. CAUSES OF UNCERTAINITY
- 7. Probability reasoning In many problem domains it is not possible to create complete, consistent models of the world.Therefore agents and people must act in uncertain worlds (which the real world is).
- 8. Reasons for reasoning probability • TRUE UNCERTAINITY:flipping a coin. • THEORATICAL IGNORANCE:There is no complete theory which is known about the problem E.g. some peculiar ( )عجیبmedical diagnosis. • LAZINESS:The space of relevant factors is very large,and would require too much work to list the complete set of antecedents()سابقہ. • Logic deals with certainities while probability deals with uncertainities.
- 10. BAYES’ RULE The Bayes’ theorem (also known as the Bayes’ rule) is a mathematical formula used to determine the conditional probability of events. Essentially, the Bayes’ theorem describes the probability of an event based on prior knowledge of the conditions that might be relevant to the event. 03 YOUR TITLE BAYES RULE
- 11. 03 YOUR TITLE PROVE BAYES’ RULE
- 12. 01 P(A|B) – the probability of event A occurring, given event B has occurred. 02 P(B|A) – the probability of event B occurring, given event A has occurred 04 P(B) – the probability of event B 03 P(A) – the probability of event A 03 YOUR TITLE
- 13. EXAMPLE OF BAYES’ RULE
- 14. 04 PROBABILITY
- 15. Probability:- • Probability can be defined as a chance that an uncertain event will occur. It is the numerical measure of the likelihood that an event will occur. The value of probability always remains between 0 and 1 that represent ideal uncertainties. • 0 ≤ P(A) ≤ 1, where P(A) is the probability of an event A. • P(A) = 0, indicates total uncertainty in an event A. • P(A) =1, indicates total certainty in an event A. •
- 16. • Event: Each possible outcome of a variable is called an event. • Sample space: The collection of all possible events is called sample space. • Conditional probability:- • Conditional probability is a probability of occurring an event when another event has already happened. • Where P(A⋀B)= Joint probability of a and B • P(B)= Marginal probability of B.
- 17. Example: • In a class, there are 70% of the students who like English and 40% of the students who likes English and mathematics, and then what is the percent of students those who like English also like mathematics? Solution: • Let, A is an event that a student likes Mathematics • B is an event that a student likes English. • Hence, 57% are the students who like English also like Mathematics.
- 18. 05 RULE BASED AND MACHINE LEARNING
- 19. RULE BASED & MACHINE LEARNING ALGORITHIM
- 20. 06 FARWORD PROBABILITY BACKWARD PROBABILITY
- 23. THANK YOU! T H A N K Y O U F O R W A T C H I N G