OBJECTIVE VERSUS SUBJECTIVE BAYESIAN INFERENCE: A COMPARATIVE STUDY.
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In Bayesian statistics, the choice of the prior distribution is often controversial. Different rules for selecting priors have been suggested in the literature, this is broadly classified into objective (non-informative) and subjective (informative) priors. A fundamental feature of the Bayesian approach to statistics is the use of prior information in addition to the (sample) data. A (proper) subjective Bayesian analysis will always incorporate genuine prior information that genuinely represents prior beliefs, which will help to strengthen inferences about the true value of the parameter and ensure that relevant information about it is not wasted. The (improper) objective Bayesian analysis is not able to do that, since the non-informative prior adds nothing to the likelihood. Data on Diabetic cases (Biomedical Laboratory Medical School University of Verona, Italy) was used for illustration.
[Emmanuel TORSEN (2015); OBJECTIVE VERSUS SUBJECTIVE BAYESIAN INFERENCE: A COMPARATIVE STUDY. Int. J. of Adv. Res. 3 (4). 0] (ISSN 2320-5407). www.journalijar.com
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