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Dealing with Uncertainty in Modeling: Validation, Verification, Accuracy, Precision

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kalani
13 diapositives · 238 Views · Jul 27, 2014

This course provides an in-depth exploration of dealing with uncertainty in modeling, covering validation, verification, accuracy, and precision aspects. Learn about different distributions, including Gaussian and Uniform distributions, and understand how to handle uncertainty in modeling scenarios to obtain reliable outcomes. The course includes practical examples to enhance understanding and application in decision-making and design processes.

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  1. A core Course on Modeling Introductionto Modeling 0LAB0 0LBB0 0LCB0 0LDB0 c.w.a.m.v.overveld@tue.nl v.a.j.borghuis@tue.nl S.27

  2. formulate purpose define Right problem? (problem validation) Right concepts? (concepts validation) identify entities choose relations conceptualize obtain values formalize relations formalize Right model? (model verification) operate model obtain result execute present result interpret result Right outcome? (outcome verification) conclude Concludephase: interpretresult Reflection: interpretresult Right answer? (answerverification)

  3. Dealing with uncertainty Validation Verification Accuracy Precision involvedealing with uncertainty, represented as a distribution. Thisgivesthe probability(density) of a particular but uncertainoutcome with someaverageandsomespreading. http://cdn.morguefile.com/imageData/public/files/j/jdurham/preview/fldr_2008_11_16/file000198601977.jpg

  4. Distributionscanbecontinuous(measuring) Dealing with uncertainty Validation Verification Accuracy Precision Gaussian(normal) distribution: the sum of sufficientlymanyuncorrelatednumbers with average andspreading has a normaldistribution. E.g.: de weightdistribution of 18-year oldAmericans. involvedealing with uncertainty, represented as a distribution. Thisgivesthe probability(density) of a particular but uncertainoutcome with someaverageandsomespreading.

  5. Distributionscanbecontinuous(measuring) or discrete (counting, e.g. dice) Dealing with uncertainty Validation Verification Accuracy Precision • Uniformdistribution: • alloutcomes in an interval between- and + have equalprobability. involvedealing with uncertainty, represented as a distribution. Thisgivesthe probability(density) of a particular but uncertainoutcome with someaverageandsomespreading.

  6. Distributionscanbecontinuous(measuring) or discrete (counting, e.g. dice) Dealing with uncertainty Validation Verification Accuracy Precision • Uniformdistribution: • alloutcomes in an interval between- and + have equalprobability. involvedealing with uncertainty, represented as a distribution. Thisgivesthe probability(density) of a particular but uncertainoutcome with someaverageandsomespreading. QUIZ What are  and  for a die?

  7. Distributionscanbecontinuous(measuring) or discrete (counting, e.g. dice) Dealing with uncertainty Validation Verification Accuracy Precision • Uniformdistribution: • alloutcomes in an interval between- and + have equalprobability. • E.g., die: =3.5, =2.5. involvedealing with uncertainty, represented as a distribution. Thisgivesthe probability(density) of a particular but uncertainoutcome with someaverageandsomespreading.

  8. Dealing with uncertainty: purpose Example 1. model usedfordecision making (e.g., diagnosis; classification ‘good’ or ‘bad’.) medium confidence low confidence high confidence http://www.clipartlord.com/wp-content/uploads/2013/04/stethoscope2.png Confidencefor diagnosis support. Compare model outcomeagainstthreshold. Confidence is lowerifareasleftand right fromtreshold are less different. Validation: is the treshhold at the right place? Does checking with thistreshholdmeananything w.r.t. the purpose? Verification (forglass box): do we calculate the distributioncorrectly? Accuracy: are we surethere is no bias? Precision: can we obtainnarrowerdistributions?

  9. Dealing with uncertainty: purpose Example 2. model usedin design: computeduncertaintyintervalsshouldbe small enoughtoassessif A or B is better. A A A B A A http://www.morguefile.com/archive/display/76905 medium confidence low confidence high confidence Confidencefor design decision support: compareone model outcomeagainst a second model outcome. Confidenceis lowerif the areas of twodistributions have larger overlap. Validation: do we compare the right properties? Verification (forglass box): do we calculate the distributioncorrectly? Accuracy: are we surethere is no bias? Precision: can we obtainnarrowerdistributions?

  10. Confidencefor black box models 72 71 70 http://www.morguefile.com/archive/display/773187 69 age (fictituous data) 68 67 66 70 5 10 15 20 25 30 35 40 45 50 55 60 65 consumed kg fries / year

  11. Confidencefor black box models 72 71 70 http://commons.wikimedia.org/wiki/File:Scatterplot_r%3D-.69.png http://www.morguefile.com/archive/display/773187 69 age (fictituous data) 68 67 66 70 5 10 15 20 25 30 35 40 45 50 55 60 65 consumed kg fries / year

  12. Confidencefor black box models QUIZ 72 What is the slope of the orange line in minutes/frie? What does thisslopemean? 71 70 http://commons.wikimedia.org/wiki/File:Scatterplot_r%3D-.69.png http://www.morguefile.com/archive/display/773187 69 age (fictituous data) 68 67 66 70 5 10 15 20 25 30 35 40 45 50 55 60 65 consumed kg fries / year

  13. Confidencefor black box models on average, peopleloose 2 years on 30 kg fries  youloose ... minutes of your life per frie Do you believe that? 72 71 70 http://commons.wikimedia.org/wiki/File:Scatterplot_r%3D-.69.png http://www.morguefile.com/archive/display/773187 69 age (fictituous data) 68 67 66 70 5 10 15 20 25 30 35 40 45 50 55 60 65 consumed kg fries / year