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]]>Many thanks for reading the post and taking the time to write a detailed comment. I’ll attempt to address the points you have made in roughly the same order that you have made them. I should also state that I’m not an expert in estimation so please feel free to correct my line of thinking.

First, my key assumption is that the estimates are made independently, so it does apply to at least some variants of the Delphi method (perhaps the pure form that you mention in your comment). However, as I have noted, the assumption of independence may well be invalid in many situations.

Second, my group consists of two estimators who make estimates independently. Therefore, the conjunctive probability (insofar as the model is concerned) is indeed given by the product of their individual estimates.

As you have stated in your excellent post, one needs two events in order to use Bayes Theorem: an independent event and an event that has (or is hypothesised to have) a dependency on the first event. In the case above, the first is the event that both estimators concur and the second is the event that they are both correct (or incorrect). The dependency in this case is not hypothesised, it is a fact – if they are both correct (or incorrect) they must concur. In effect this is a degenerate case of Bayes Theorem; one where the dependency is known.

Finally, I reiterate that my model is simplistic and have noted several caveats to this effect in my post.

Many thanks again for taking the time to read and comment.

Regards,

Kailash.

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