Jeffrey′s Noninformative Prior in Bayesian Conjoint Analysis

  • Oh, Man-Suk (Ewha Womans University) ;
  • Kim, Yura (IT Service Team/Information Analysis Part, LG Capital)
  • 발행 : 2000.06.01


Conjoint analysis is a widely-used statistical technique for measuring relative importance that individual place on the product's attributes. Despsite its practical importance, the complexity of conjoint model makes it difficult to analyze. In this paper, w consider a Bayesian approach using Jeffrey's noninformative prior. We derive Jeffrey's prior and give a sufficient condition under which the posterior derived from the Jeffrey's prior is paper.


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