Variations on Bayesian Prediction and Inference

Abstract

A Bayesian approach, based on updating prior information in light of new observations, via Baye's formula, has both nice intuition and strong theoretical support. However, in some applications, there are some roadblocks to carrying out the Bayesian analysis as usual. This ARO-sponsored project considered two general types of these settings. First, for the problem of predicting a future observation, a flexible Bayesian model is available but is too computationally expensive to implement when data are streaming and fast prediction is required.

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Document Details

Document Type
Technical Report
Publication Date
May 09, 2016
Accession Number
AD1015363

Entities

People

  • Ryan G. Martin

Organizations

  • University of Illinois at Chicago

Tags

Communities of Interest

  • Biomedical
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Computations
  • Computer Science
  • Data Science
  • Estimators
  • Information Science
  • Machine Learning
  • Mathematics
  • Monte Carlo Method
  • Probability
  • Standards
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • Students

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference