Information and Decision Sciences

IDS 446 Decision Analysis

Credit hours 3 - Undergraduate 4-Graduate
Catalog description Prior and posterior distributions, conjugate priors, value of information, applications to decision making in business.
Key topics
  • Review of classical probability, binomial distribution and normal
    distribution
  • Betting behavior, lotteries, odds, subjective probability
  • Conditional probability, Bayes Theorem
  • Bayesian inference for discrete models
  • Bayesian inference for continuous models
  • Prior distributions, assessment of prior distributions; conjugate priors for normal and binomial families
  • Payoffs, losses, loss of opportunity
  • Criteria for decision-making: minimax, maxmin, expected value,
    applications to business problems
  • Value of information
  • Linear payoff functions
Prerequisites for this course IDS 371
Course(s) for which this is a prerequisite None
Required course for None
Elective course for Selective course for IDS Majors
Frequency of offering Spring
Recent offerings, instructors, syllabi None
Planned offerings None
Cross-listings None
Archived Syllabi None
Comments None