Bayesian approach

Bayesian approach

An approach to data analysis which provides a posterior probability distribution for some parameter (e.g., treatment effect) derived from the observed data and a prior probability distribution for the parameter. The posterior distribution forms the basis for statistical inference.
References in periodicals archive ?
A Bayesian approach to the linear-model with unequal variances.
Below we provide a basic description of the Bayesian approach but readers who want more information may wish to consult those articles.
The results of their experiment indicate that the Bayesian approach is superior whilst the conventional neural network approach offers little or no advantage over logistic regression.
Regulators generally acknowledge that a Bayesian approach is a more sensible method than a classical approach when performing a cost-benefit analysis for a risk with a known probability distribution; an "uncertain" risk.
The Bayesian approach is appropriate in assessing medication compliance among glaucoma patients because of the following reasons:
Cairns (1995) describes the Bayesian approach as a way of incorporating parameter uncertainty in the "Modelling Process" and applies the approach to rain theory's adjustment coefficient.
It is questionable, therefore, whether a defence of this Bayesian approach against its critics should have been left until the final chapter.
In the Bayesian approach, the parameters are regarded as random variables having a known distribution [p([Theta])].
Hence, Binmore concludes, the Bayesian approach of assuming that agents have priors in an incomplete universe is utterly unreasonable.
Scientific Reasoning: The Bayesian Approach by Colin Howson and Peter Urbach, Open Court, 1989 is an excellent example of this point of view.
In the Bayesian approach, this historical material can be formally incorporated into the analysis.
It seems, on the basis of the examples we have considered, that our formally Bayesian approach to the analysis of data on density and visibility can give very sensible and appropriately hedged estimates, but we think that the conclusions should be analyzed cautiously.
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