[IP3-4-2-1] Conditional probability

Bayes’s theorem is an extremely powerful means of using information at hand to estimate probabilities of outcomes related to the occurrence of preceding events. Bayes' Theorem uses a priori (subjective) and conditional probabilities to calculate the probability of an uncertain event occurring. A priori probabilities represent what the modeler believes, before testing, to be the probability of an event occurring. Conditional probabilities are probabilities that other events occur in conjunction with the original event.

Introduction

External resources

• Campbell, J.B., & Wynne, R.H. (2011). Introduction to remote sensing. Guilford Press
• Hepinstall, J. A., & Sader, S. A. (1997). Using Bayesian statistics, Thematic Mapper satellite imagery, and breeding bird survey data to model bird species probability of occurrence in Maine. Photogrammetric engineering and remote sensing, 63(10), 1231-1236.

Learning outcomes

• 2355 - Identify different methods that employ conditional probability for image classification

  Identify different methods that employ conditional probability for image classification

Self assessment

Outgoing relations

• [IP3-4-2-1] Conditional probability subclassof [IP3-4] Image classification

Incoming relations

• [AM7-7] Bayesian methods subclassof [IP3-4-2-1] Conditional probability