AbstractBayesian predictive procedures give the researcher a very appealing method to evaluate the chances that the experiment will end up showing a conclusive result, or on the contrary an inconclusive one. The prediction can be explicitly based on either the hypothesis used to monitor the experiment expressed either in terms of prior distribution, on partially available data, or on both. In this paper, we propose a Bayesian predictive methodology based on two steps which can be used to develop an adaptive design for the experimental trials. This procedure does not require intensive computation and comprehensive simulations. We have used the non-informative prior to give evidence on the objectivity of the experimental data.
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