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Bayesian Analysis of Linear and Non‐Linear Population Models by Using the Gibbs Sampler

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  • J. C. Wakefield
  • A. F. M. Smith
  • A. Racine‐Poon
  • A. E. Gelfand

Abstract

A fully Bayesian analysis of linear and non‐linear population models has previously been unavailable, as a consequence of the seeming impossibility of performing the necessary numerical integrations in the complex multiparameter structures that typically arise in such models. It is demonstrated that, for a variety of linear and non‐linear population models. a fully Bayesian analysis can be implemented in a straightforward manner by using the Gibbs sampler. The approach is illustrated with examples involving challenging problems of outliers and mean–variance relationships in population modelling.

Suggested Citation

  • J. C. Wakefield & A. F. M. Smith & A. Racine‐Poon & A. E. Gelfand, 1994. "Bayesian Analysis of Linear and Non‐Linear Population Models by Using the Gibbs Sampler," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 201-221, March.
    • Handle: RePEc:bla:jorssc:v:43:y:1994:i:1:p:201-221
    DOI: 10.2307/2986121
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    Cited by:

    1. Justin L. Tobias & Mingliang Li, 2004. "Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures," Journal of Economic Surveys, Wiley Blackwell, vol. 18(2), pages 153-180, April.
    2. Alain Pirotte & Jean-Loup Madre, 2011. "Determinants of Urban Sprawl in France," Urban Studies, Urban Studies Journal Limited, vol. 48(13), pages 2865-2886, October.

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