Bayesian theorem is the theoretical basis of uncertainty management as well as the stochastic foundation for forecast-oriented expert systems. Mathematically, the reasoning steps can be represented by a sequence of probabilistic computations. To reduce the mathematical complexity and make it mentally manageable, an assumption, known as Bayesian Assumption, is usually made. This assumption does simplify the computation, but also introduces errors to the computation and makes it distorted from the real probabilistic result. In this paper, I use Venn diagrams to discuss the distortion being introduced to the result by showing cases from best-fitted, partial-fitted to worst-fitted .
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Eliciting an overlooked aspect of Bayesian reasoning
Yang, Feng-Jen
Follow ACM SIGCSE Bulletin
, Volume 39 (4)
Association for Computing Machinery – Dec 1, 2007
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