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Glossary: Building Decision Trees

2022

This glossary of terms supports the teaching pack on Building Decision Trees, in which students learn how to structure the elements (e.g., objectives, alternatives, probabilities, and outcomes) of a problem into a decision tree model, conduct a baseline analysis of the expected value of different alternatives, assess the value of perfect information, and perform sensitivity analyses. In addition to the glossary, materials include an instructor's note, videos, companion slides, an annotated bibliography, and sample exercises.

Glossary of Terms

  • Averaging and folding back
  • Chance node
  • Collectively exhaustive
  • Conditional probability
  • Consequence
  • Decision node
  • Decision tree
  • Expected value
  • Expected value of information
  • Joint probability
  • Mutually exclusive
  • Outcome
  • Parameter uncertainty
  • Prevalence
  • Prior probability
  • Posterior probability
  • Probability
  • Sensitivity analysis
  • Subjective probability
  • Terminal node
  • Threshold analysis

This teaching pack was developed by Sue J. Goldie at the Center for Health Decision Science, Harvard T.H. Chan School of Public Health. The multimedia components were developed as part of a series of pilots in the CHDS Media Hub, led by Jake Waxman, where media-based pedagogy experiments contribute to new ways of thinking about short form content.

 

Related Files:

Source:

Glossary. Teaching Pack: Building Decision Trees. Center for Health Decision Science, Harvard T.H. Chan School of Public Health 2022. https://repository.chds.hsph.harvard.edu/repository/2500