Principle of transformation groups
The principle of transformation groups is a rule for assigning prior probabilities in a statistical inference problem. It was first suggested by E. T. Jaynes and can be seen as a generalization of the principle of indifference.
| Part of a series on |
| Bayesian statistics |
|---|
| Posterior = Likelihood × Prior ÷ Evidence |
| Background |
| Model building |
| Posterior approximation |
| Estimators |
| Evidence approximation |
| Model evaluation |
Prior probabilities determined according to the principle are objective in the sense that they do not incorporate any information beyond the features of the problem itself, so that two people who apply the principle to the same problem will assign the same prior probabilities. Therefore, the principle forms part of the objective Bayesian interpretation of probability.
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