Nonparametric Bayesian Bioassay Including Ordered Polytomous Response

Abstract

Previous attempts at implementing fully Bayesian nonparametric bioassay have enjoyed limited success due to computational difficulties. We show here how this problem may be generally handled using a sampling based approach to develop desired marginal posterior distributions and their features. A useful extension is presented which treats the case of ordered polytomous response. Illustrative examples are provided.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 14, 1992
Accession Number
ADA255990

Entities

People

  • Alan E. Gelfand
  • Lynn Kuo

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Assays
  • Bayesian Inference
  • Bayesian Networks
  • Bioassay
  • Data Analysis
  • Data Mining
  • Data Science
  • Information Science
  • Monte Carlo Method
  • Probability
  • Sampling
  • Standards
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference