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Statistical techniques used in practical data analysis. e.g. t-tests, ANOVA, regression, correlation;

The use of probabilistic models in psychology and linguistics

Machine learning and computational linguistics/NLP

Measure theory (in fact, almost anything involving infinite sets)

Using logic and probability to handle uncertain situation

Probability based reasoning is same as understanding directly from the knowledge that a given probability rating based on uncertainty present

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Engineering

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- 1. Probabilistic Reasoning SushantGautam 072BCT544@ioe.edu.np IOE, Pulchowk Campus
- 2. • Statistical techniques used in practical data analysis. e.g. t-tests, ANOVA, regression, correlation; • The use of probabilistic models in psychology and linguistics • Machine learning and computational linguistics/NLP • Measure theory (in fact, almost anything involving infinite sets) PROBABILITY& STATISTICS
- 3. • Using logic andprobabilityto handleuncertainsituation • Probabilitybasedreasoning is same as understanding directlyfrom the knowledgethat agiven probability rating basedon uncertaintypresent PROBABILITYREASONING
- 4. • Crossing the street in traffic. We’ve all done this: UNCERTAINTY AND UNCERTAIN REASONING : Intuition warmup you’re in a hurry, so instead of waiting for the “walk” sign you look both ways and see that the nearest cars are far enough away that you can cross safely before they arrive where you are. You start walking and (I’m guessing) make it across just fine.
- 5. • The cop and the man in the window. UNCERTAINTY AND UNCERTAIN REASONING : Intuition warmup You’re a police officer out on patrol late at night. You hear an alarm go off and follow the sound to a jewelry store. When you arrive, you see a broken window and a man crawling out of it wearing black clothes and a mask, carrying a sack which turns out to be full of jewelry.
- 6. • Medical diagnosis 1. the person has a cold (h1), 2. lung disease (h2), 3. heartburn (h3). 4. tuberculosis (h4) UNCERTAINTY AND UNCERTAIN REASONING : Intuition warmup Suppose we observe a person coughing, and we consider three hypotheses as explanations.
- 7. • Doing science requires the ability to cope with uncertainty UNCERTAINTY • Human familiar deductive logic is great for reasoning • With science & machines, we need a procedure for determining which conclusions to draw This should take the form of an inductive logic.
- 8. PROBABILITY
- 9. PROPOSITION
- 10. WHYUSE PROBABILITY ?
- 11. BAYER’SRULE
- 12. NEURAL NET
- 13. DEEPLEARNING
- 14. LIMITATIONS OFDEEPLEARNING
- 15. MACHINELEARNING vs PROBABILISTICREASONING
- 16. ONE SLIDE ON BAYSIAN MACHINE LEARNING
- 17. WHYSHOULD WECARE ?
- 18. BEINGBAYESIAN
- 19. BAYESIANDEEPLEARNING
- 20. WHYIS PROBABILISTICAPPROACH ESSENTIAL?
- 21. WHYDOES UBERCARE?
- 22. Probabilistic Reasoning
- 23. PROBABILISTICPROGRAMMING
- 24. Is Charlie horse?
- 25. UNCERTAINTY
- 26. HANDLING UNCERTAINTY
- 27. • Probabilistic reasoningare used when outputor outcomes are unpredictable • One way to express confidenceabout such event is probability • Probabilityof an uncertainevent ismeasure of degree of likelihood of occurrence of that event PROBABILITYREASONING : HANDLING UNCERTAINTY
- 28. To aid in the interpretation of gene lists, PheNetic was built on top of ProbLog. ProbLog is used to reason over heterogeneous data sources like the Helsinki Biomine database.
- 29. • Uncertaintyin probabilisticreasoningarisesdue to inability topredictoutcomesdue to unreliable, incompleteor in consistenceknowledge PROBABILITYREASONING
- 30. UNCERTAINTY “All swans are white”, a universal generalization from observation of many, many white swans This was before Europeans went to Australia. When they got there, they discovered that Australian swans are black. D’oh! Early modern philosophy in Europe
- 31. OTHER UNCERTAINTY SOURCES •Human Error •Experimental Error •Random Error •Equipment Error •Environment Variation
- 32. NEEDOF PROBABILISTIC REASONING Cognitive sciences (e.g. linguistics, psychology, AI, philosophy of mind & epistemology): we’re trying to understand human intelligence, using noisy and uncertain information to make (hopefully) reasonable/ intelligent decision
- 33. START(x) -> END(x). START(“Presentation”). ? END(“Presentation”). TRUE

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