26and27_Overheads - Objectives for today Review of...

Info iconThis preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon
1 ACC 333 (Farrel ) 1 Objectives for today Review of probability basics – Definitions – Types of probabilities Decision trees – What they are and how to draw them – Using them to evaluate decision alternatives – Is gathering additional information worth the cost? • Value of perfect and imperfect information ACC 333 (Farrel ) 2 Probability – Definitions Probability –The likelihood (i.e., chance) of occurrence of an event • Probability of some event A is denoted P(A) • Ranges from 0 through 1 Probability distribution – An exhaustive list of all events that can result from a chance process and the probability associated with each of those events • For example, what’s the probability distribution for the roll of a die?
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2 ACC 333 (Farrel ) 3 Probability – Types Marginal probability – Probability of an event A i occurring across all conditions of event B (or vice versa) Joint probability – Probability of two events occurring together • Denoted P(A,B) or P(A B) ACC 333 (Farrel ) 4 Probability – Types (continued) Joint and marginal probabilities can be easily computed with a joint probability table A 1 A 2 Total B 1 B 2 Total 1.00 joint probabilities marginal probabilities marginal probabilities
Background image of page 2
3 ACC 333 (Farrel ) 5 Probability – Types (continued) Conditional probability – Probability of an event occurring, given that another event has already occurred • Denoted P(B | A) • Read as "the probability that B will occur, given that A has occurred" • Computed as: P(B|A) = P(A B) P(A) ACC 333 (Farrel ) 6 Probability – Types (continued) Prior probability Initial probability estimate of an event • e.g., Probability that a customer will default on a loan Posterior probability Revised probability estimate of an event after receiving some relevant information that allows you to update the initial estimate • e.g., Revised probability that a customer will default on a loan, now that I know the customer has missed a payment
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
4 ACC 333 (Farrel ) 7 Probability – Types (continued) Posterior probabilities can be computed with Bayes' Theorem P(A 1 |B) = P(A 1 )P(B|A 1 ) x P(A 1 )P(B|A 1 ) + P(A 2 )P(B|A 2 ) Note: If you need this formula on the exam, I will provide it in this general form. ACC 333 (Farrel )
Background image of page 4
Image of page 5
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 03/19/2012 for the course ACCACC 333 taught by Professor Anne during the Fall '11 term at Miami University.

Page1 / 17

26and27_Overheads - Objectives for today Review of...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online