Bayesian Estimation

Dr Yeoh is just finishing his presentation on item response theory. The audience is quiet, so the moderator asks a question.

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 Dr Yeoh: So in summary, computer adaptive testing works like this: the computer analyzes responses of a student, if the student gets it right, a slightly more difficult question is put up next on the screen, and so on until it gets a reading on the proficiency level of the student.

Moderator: Does the computer know if the student is guessing?

Dr Yeoh: That can be screened for by analyzing previous student responses. After that, this stochastic approach comes in. We can use a Bayesian Estimation if we know something about the background or history of the test-takers. Or we can use a Maximum Likelihood Estimation if we don’t know anything about the test-taking population.

Moderator: Can you give us an example?

Dr Yeoh: OK, like this. If the sky is already cloudy it will help make a prediction on whether it will rain or not. That’s Bayesian. But if we are remote, we don’t know whether the sky is cloudy or not, and we make a prediction based on previous cases, the history of the area, assuming a normal distribution, that’s Maximum Likelihood Estimation.

Moderator: That’s all we have time for. Please remember there are two discussion rooms. There is this fantastic room, E1, with hi-tech presentations, but the other one is, the other room, E2, is ah, wonderful too.

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Voice-over

One of the moderator’s tasks is to make a presentation conclude smoothly. Clarifying questions such as “Can you give us an example?” are a reliable standby.

Note the moderator’s dropped guard, flagged by the contrastive conjunction “but”, in expressing unconscious preference for Room E1 over E2.

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