Abstract
In every real-world domain where reasoning under uncertainty is required, combining information from different sources (‘experts’) can be really a powerful tool to enhance accuracy and precision of the ‘final’ estimate of the unknown quantity. Bayesian paradigm offers a coherent perspective which can be used to address the problem, but an issue strictly related to information combining is how to perform an efficient process of sequential consulting: at each stage, the investigator can select the ‘best’ expert to be consulted and choose whether to stop or continue the consulting. The aim of this paper is to rephrase the Bayesian combining algorithm in a sequential context and use Mathematica to implement suitable selecting and stopping rules.
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Notes
- 1.
Due to the reciprocity of the stochastic independence assumption ii) can be also expressed as invariance to scale about θ, that is \(h\left (\theta \left \vert v^{\left (k\right )}\right.\right ) = h\left (\theta \right )\): the event v (k) alone gives no information regarding θ.
- 2.
In fact, all the other elements being equal, the more A is uncertain about θ, the more an answer m j is worthy.
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Agati, P., Stracqualursi, L., Monari, P. (2014). Sequential Combining of Expert Information Using Mathematica. In: Melas, V., Mignani, S., Monari, P., Salmaso, L. (eds) Topics in Statistical Simulation. Springer Proceedings in Mathematics & Statistics, vol 114. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2104-1_2
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DOI: https://doi.org/10.1007/978-1-4939-2104-1_2
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