Skip to main content
SpringerLink
Log in

Maximum Entropy and Bayesian Methods Santa Barbara, California, U.S.A., 1993

  • Book
  • © 1996

Overview

Editors:
  1. Glenn R. Heidbreder

    1. Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Fundamental Theories of Physics (FTPH, volume 62)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (33 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  3. Bayesian Hyperparameters

    1. Hyperparameters: Optimize, or Integrate Out?

      • David J. C. MacKay
      Pages 43-59

    2. What Bayes has to Say about the Evidence Procedure

      • David H. Wolpert, Charlie E. M. Strauss
      Pages 61-78

    3. Reconciling Bayesian and Non-Bayesian Analysis

      • David H. Wolpert
      Pages 79-86

  4. Bayesian Robustness

    1. Bayesian Robustness: A New Look from Geometry

      • Carlos C. Rodríguez
      Pages 87-96

    2. Local Posterior Robustness with Parametric Priors: Maximum and Average Sensitivity

      • Sanjib Basu, Sreenivasa Rao Jammalamadaka, Wei Liu
      Pages 97-106

  5. Clustering

    1. Tree-Structured Clustering via the Minimum Cross Entropy Principle

      • David Miller, Kenneth Rose
      Pages 107-120

  6. Inverse Problems

    1. A Scale Invariant Bayesian Method to Solve Linear Inverse Problems

      • Ali Mohammad-Djafari, Jérôme Idier
      Pages 121-134

    2. Maximum Entropy Signal Transmission

      • Enders A. Robinson
      Pages 135-147

  7. Quantum Probability Theory

    1. Maximum Quantum Entropy for Classical Density Functions

      • Timothy C. Wallstrom
      Pages 149-155

    2. Smoothing in Maximum Quantum Entropy

      • Timothy C. Wallstrom
      Pages 157-159

    3. Density Estimation by Maximum Quantum Entropy

      • R. N. Silver, T. Wallstrom, H. F. Martz
      Pages 161-174

  8. Philosophy

    1. Belief and Desire

      • Anthony J. M. Garrett
      Pages 175-186

  9. Computational Issues

    1. A Bayesian Genetic Algorithm for Calculating Maximum Entropy Distributions

      • Neil Pendock
      Pages 187-195

    2. A Mathematica™ Package for Symbolic Bayesian Calculations

      • Paul Desmedt, Ignace Lemahieu, K. Thielemans
      Pages 197-203

    3. A Multicriterion Evaluation of the MemSys5 Program for PET

      • Paul Desmedt, Ignace Lemahieu, Koen Bastiaens
      Pages 205-211

    4. Parallel Maximum Entropy Reconstruction of PET Images

      • Koen Bastiaens, Paul Desmedt, Ignace Lemahieu
      Pages 213-219

  10. Applications

    1. Bayesian Non-Linear Modeling for the Prediction Competition

      • David J. C. MacKay
      Pages 221-234

    2. Bayesian Modeling and Classification of Neural Signals

      • Michael S. Lewicki
      Pages 235-253
Back to top

Keywords

About this book

Maximum entropy and Bayesian methods have fundamental, central roles in scientific inference, and, with the growing availability of computer power, are being successfully applied in an increasing number of applications in many disciplines. This volume contains selected papers presented at the Thirteenth International Workshop on Maximum Entropy and Bayesian Methods. It includes an extensive tutorial section, and a variety of contributions detailing application in the physical sciences, engineering, law, and economics.
Audience: Researchers and other professionals whose work requires the application of practical statistical inference.

Editors and Affiliations

  • Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, USA

    Glenn R. Heidbreder

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation