1 / 24

Applying Probability

Applying Probability. Define problem of interest in terms of “random variables” and/or “composite events” Use real world knowledge, symmetry to associate probs in [0,1] with ‘elementary events’ all probs are conditional on real world knowledge Use consistent prob rules

tori
Download Presentation

Applying Probability

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Applying Probability • Define problem of interest • in terms of “random variables” and/or “composite events” • Use real world knowledge, symmetry • to associate probs in [0,1] with ‘elementary events’ • all probs are conditional on real world knowledge • Use consistent prob rules • To associate probs with rand vars/ comp events • Multiplication and Addition Rules ST2004 Week 7

  2. Probability We give more emphasis to ‘event identities’. Book in Ch 7 uses more math shortcuts (binomial coeffs) and  notation than we will use. Best immediate preparation is Q1-12 in Ch 1. Formulate and approach via EXCEL before attempting probability solution. • Prob Rules Week 7 • Basic in text, Ch2.2 • Conditional ProbBayes Rule in Ch 6 • Fuller treatment in Ch 7 8 • Discrete Prob Dist Week 8 • Ch 4 – see lab on Queuing • Ch 9 • Continuous Prob Dist Week 9 • Ch 5 Normal dist • Ch 10 ST2004 Week 7

  3. Problems • Dice: Seek prob dist of M2,S2 ,M3,S3 ,Mk,Sk • Later E(S2) Var(S2) etc • Mini-league: Seek prob dist of (NA, NB, NC) when • Pr( A beats B)=2 Pr(B beats A)  Pr( A beats B)=? • Pr( A beats B)=pAB; similarly pBC, pAC • Later E(NA),Var(NA) and E(NA|NC=0),Var(NA|NC=0) ST2004 Week 7

  4. Events, Random Vars, Sample Space and Probability Rules Event A Simplest Random Variable Values of A are TRUE/FALSE Random Variable Y Values of Y are y1, y2..yk(sample space; exhaustive list) Events such as (Y= y)

  5. Event Identities Re-express compound events in and/or combinations of elementary events Coin (H orT) Experiment Happened Cards Ace  (A♠ orA♥or A♣ or A♦) Redand (NOT♦)  (2♥or.. or A♥) Event Algebra

  6. Re-express in terms of and/or combs of (..) (elementary events and/or simple compound events). Often there is more than one way. “A out-right winner of league”. Use as elementary events Outcomes of games A/B, etc, and as relatively simple compound events, the scores NA , etc “At least one Queen in two cards” “Max of 3 dice is 3” and “Max of 3 dice is  3” “Sun of 3 is 4” Event Identities

  7. Event Identities ST2004 Week 7

  8. Event Identities ST2004 Week 7

  9. Probability Rules Plus real world knowledge Addition Rule ST2004 Week 7

  10. Coins/Dice/Cards ST2004 Week 7

  11. Applying Prob Rules Generalisation of Addition Rule ST2004 Week 7

  12. Event Identities: Password Elementary events and associated probs Pr(Dup) via addition rules ST2004 Week 7

  13. Conditional Probability ST2004 Week 7

  14. Probability RulesConditional Prob and Independence Multiplication Rule ST2004 Week 7

  15. Decomposing with CondProbs ST2004 Week 7

  16. Applying Cond Probability Rules ST2004 Week 7

  17. Applying Cond Probability Rules Write down event identities explicitly Justify use of + or  explicitly ST2004 Week 7

  18. Bayes Rule & Thinking Backwards See text, Ch 8.2 ST2004 Week 7

  19. Bayes Rule & Thinking Backwards ST2004 Week 7

  20. Bayes Rule & Thinking Backwards ST2004 Week 7

  21. Probability Distributionsand Random Variables Main use of probability • Output of a simulation exercise (thought expt) • Columns defined random variables Y • Discrete countable list of possible values • Continuous values • True/False values Random Var is ‘Event’ • Discrete random vars fully described by • 2 lists Poss Values y of Y Associated Probs Pr(Y=y) ST2004 Week 7

  22. Applying Probability Rules – Indep Case ST2004 Week 7

  23. Applying Probability Rules – Indep Case ST2004 Week 7

  24. Conditional Distributions ST2004 Week 7 Probabilities must sum to 1!

More Related