BAYES DECISION PROCEDURES FOR STIMULUS SAMPLING MODELS: I. NONSEQUENTIAL EXPERIMENTATION.
Abstract
In stimulus sampling models of learning, the probability distribution defined on the sequence of conditioning functions which are used in these models may be regarded as a distribution over parameters. Consequently, this probability distribution is interpreted as an a priori distribution and the appropriateness of Bayes decision procedures for solving statistical decision problems involving these models is shown. Using beta distributions as a tractable family of prior distributions over the parameters of the single element model, Bayes solutions are illustrated to: (1) the learning criterion problem, (2) parameter estimation problems, and (3) the optimal design of a learning experiment. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 28, 1964
- Accession Number
- AD0606162
Entities
People
- Robert E. Dear
Organizations
- System Development Corporation