Papers by Keyword: Bayesian Statistic

Abstract: This paper reviews several current methods of calculating buffer on the basis of pointing out each merits and pitfalls and then introduces Bayesian statistical approach to CCS / BM domain to calculate the size of the project buffer, to overcome that the current method of the buffer calculation is too subjective and the defect on lacking of practical application. In Crystal Ball, we compare the simulation results of implementation process on the benchmark of C&PM, RESM and SM. The results show that the buffer using this method can ensure the stability of the project’s completion probability, and this method has great flexibility.

Abstract: Bayesian statistics provides a powerful tool for the analysis of data. The methods are flexible enough to permit a realistic modelling of complex measurements. Prior information about the experiment, as well as knowledge from other sources can be used in a natural way. All relevant quantities concerning the measurement, as e. g. the expected values and their associated uncertainties are obtained from probability density functions. Bayesian data analysis strictly follows the rules of probability theory, thus ensuring that the procedure is free of inconsistencies and is in accordance with the Guide to the Expression of Uncertainty in Measurement (GUM).