Hierarchical Bayesian estimation
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- | + | Hierarchical bayesian is an estimation procedure used to estimate coefficients (βs) of behavioral models like mixed logit. Therefore, hierarchical bayesian is ''not'' a new behavioral model beside logit and probit, but only an alternative to for example maximum likelihood method. | |
- | the | + | HB (Hierarchical Bayesian) method has root in the Bayesian probability [http://en.wikipedia.org/wiki/Bayesian_probability]. However, according to Bermstein-Von Mises Theorem it can be interpreted completely in the classical probability perspective [http://en.wikipedia.org/wiki/Classical_definition_of_probability]. |
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Revision as of 14:53, 12 December 2007
Hierarchical bayesian is an estimation procedure used to estimate coefficients (βs) of behavioral models like mixed logit. Therefore, hierarchical bayesian is not a new behavioral model beside logit and probit, but only an alternative to for example maximum likelihood method.
HB (Hierarchical Bayesian) method has root in the Bayesian probability [1]. However, according to Bermstein-Von Mises Theorem it can be interpreted completely in the classical probability perspective [2].