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Bayesian Inference for Linear Models Subject to Linear Inequality Constraints

In: Modelling and Prediction Honoring Seymour Geisser

Author

Listed:
  • John F. Geweke

    (Federal Reserve Bank of Minneapolis, Research Department
    University of Minnesota, Department of Economic)

Abstract

The normal linear model, with sign or other linear inequality constraints on its coefficients, arises very commonly in many scientific applications. Given inequality constraints Bayesian inference is much simpler than lassical inference, but standard Bayesian computational methods become impractical when the posterior probability of the inequality constraints (under a diffuse prior) is small. This paper shows how the Gibbs sampling algorithm can provide an alternative, attractive approach to inference subject to linear inequality constraints in this situation, and how the GHK probability simulator may be used to assess the posterior probability of the constraints.

Suggested Citation

  • John F. Geweke, 1996. "Bayesian Inference for Linear Models Subject to Linear Inequality Constraints," Springer Books, in: Jack C. Lee & Wesley O. Johnson & Arnold Zellner (ed.), Modelling and Prediction Honoring Seymour Geisser, pages 248-263, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4612-2414-3_15
    DOI: 10.1007/978-1-4612-2414-3_15
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    Cited by:

    1. Jialei Yu & Min Xu & Jinyu Chen, 2026. "Bayesian modal regression with linear inequality constraints using mixture distributions," Computational Statistics, Springer, vol. 41(3), pages 1-26, April.
    2. Dimitris Korobilis, 2025. "Exploring Monetary Policy Shocks with Large-Scale Bayesian VARs," Papers 2505.06649, arXiv.org.

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