Bayesian Inference for Linear Models Subject to Linear Inequality Constraints

John F. Geweke
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John F. Geweke: Federal Reserve Bank of Minneapolis, Research Department

A chapter in Modelling and Prediction Honoring Seymour Geisser, 1996, pp 248-263 from Springer

Abstract: 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.

Keywords: Posterior Probability; Posterior Distribution; Inequality Constraint; American Statistical Association; Multivariate Normal Distribution (search for similar items in EconPapers)
Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-2414-3_15

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DOI: 10.1007/978-1-4612-2414-3_15

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