Bayesian Information Criterion - Characteristics of The Bayesian Information Criterion

Characteristics of The Bayesian Information Criterion

  1. It is independent of the prior or the prior is "vague" (a constant).
  2. It can measure the efficiency of the parameterized model in terms of predicting the data.
  3. It penalizes the complexity of the model where complexity refers to the number of parameters in model.
  4. It is approximately equal to the minimum description length criterion but with negative sign.
  5. It can be used to choose the number of clusters according to the intrinsic complexity present in a particular dataset.
  6. It is closely related to other penalized likelihood criteria such as RIC and the Akaike information criterion.

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