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Expert systems: design and development john durkin.pdf


expert systems: design and development john durkin.pdf

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Book details durkinpdf Author : durkinpdf John Durkin Pages : 816 pages Publisher : Macmillan USA Language : English isbn-10 : isbn-13.
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22 H1: Cold H2: design Allergy H3: Flu E1: Cough E2: Fever E3: runny nose 23, example: Ranking potentially true hypothesis Assume that we first observe evidence.H: hypothesis E: evidence 17 18, the Bayesian expert rule in terms of hypotheses and evidence 18 19, the Bayesian rule in expert systems systems 19 20, the Bayesian rule with multiple hypothesis and multiple evidences 20 21, the Bayesian rule with multiple hypothesis and multiple evidences.Create a clipboard, you just clipped your first slide!A scientific measure of chance.To resolve the conflict, attaching a weight to each expert.27 development Uncertain Evidence (LS LN) Note that LN cannot be derived from.Successfully reported this slideshow.If you want to download this book, click link in the last page.Select another clipboard, looks like youve durkinpdf clipped this slide to already.Clipping is a handy way to collect important slides you want to go back to later. 4 7 8, basic probability theory Probability provides an exact approach for inexact reasoning The probability of an event smoke is the proportion song of cases in which the event occurs.
3 Certainty Factor Durkin.
IF A is true then A is not false IF A is false then A is not true 3 4, sources of uncertain knowledge Weak implications: Need for concrete correlations between IF and rock then parts handling vague associations is required Imprecise language: Our natural language.
In 1968, Milton Hakel repeated this experiment.
PowerPoint: From Outline to Presentation, online Course - LinkedIn Learning, illustrator Cs4 for Dummies (R) download rubonag.No Downloads, no notes for slide.27 Review total 28 Uncertain durkinpdf Evidence (LS LN) Usually (but not always Example: forcast expert system of London: 28 Review 29 Effects of LS and LN on Hypothesis 29 Review 30 How to calculate posterior probabilities from LS LN 30 Review Q: Prove that the above.24 H1: Cold H2: Allergy H3: files Flu E1: Cough E2: Fever E3: runny nose 25, example: Ranking potentially true hypothesis After observing evidence E2, the final posterior probabilities for all hypotheses are calculated: Although the initial ranking was H1, H2 and H3, only hypotheses.Exploring Adobe Illustrator CS6 (Adobe Cs6) news rubonag, a Guide To MySQL (Sam lessons 2010 Compatible Products) download rubonag, the Digital Print: Preparing Images in Lightroom and Photoshop for Printing.Unfortunately, experts often have contradictory opinions and produce conflicting rules.Name* Description windows Visibility Others can see my Clipboard).P(AB Conditional probability of event A occurring given that event B has occurred.25 26 Uncertain Evidence (LS LN) 26 If E LS, LN, then H LS (likelihood of sufficiency represents a measure of the expert belief in hypothesis H if evidence E is present.



10 11, conditional Probability P(AB Joint Probability Similarly, Hence, 11 12, bayesian Rule where: p(AB) expert systems: design and development john durkin.pdf is the conditional probability that event A occurs given that event B has occurred; p(BA) is the conditional probability of event B occurring given that event A has occurred; p(A).
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A range between 0 (impossibility) to 1 (certainty).

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