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21 28 29 Mean differences or log odds ratios were modelled using non-informative prior distributions.
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Bayesian model using non-informative priors on the headache rate was used.
While the estimates of population size and trend did not suffer greatly in models using non-informative priors, the algorithm was unable to accurately estimate demographic parameters.
To minimize the influence of prior beliefs on the analysis, all model parameters were estimated using non-informative prior distributions.
The estimation of these two models was performed through Bayesian methods using non-informative prior distributions for the respective parameters.
Ashcar (1993) derived the approximate Fisher information matrix for the parameters α and β and considered Bayesian inference using non-informative and Jeffrey's priors.
The parameters can be then estimated within the Bayesian framework (MCMC) using non-informative priors for the parameters.
We used non-informative priors in Bayesian models, as previously published [22].
Similar results were obtained when we used non-informative priors instead of expert opinion to estimate the Bayesian model.
We used non-informative priors for all parameters.
We used non-informative priors for all parameter estimations.
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