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Part of a series on
Bayesian statistics
Posterior = Likelihood × Prior ÷ Evidence
Background
Bayesian inference
Bayesian probability
Bayes' theorem
Bernstein–von Mises theorem
Coherence
Cox's theorem
Cromwell's rule
Principle of indifference
Principle of maximum entropy
Model building
Weak prior ... Strong prior
Conjugate prior
Linear regression
Empirical Bayes
Hierarchical model
Posterior approximation
Markov chain Monte Carlo
Laplace's approximation
Integrated nested Laplace approximations
Variational inference
Approximate Bayesian computation
Estimators
Bayesian estimator
Credible interval
Maximum a posteriori estimation
Evidence approximation
Evidence lower bound
Nested sampling
Model evaluation
Bayes factor
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Posterior predictive
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The principle of transformation groups is a methodology for assigning prior probabilities in statistical inference issues, initially proposed by E. T. Jaynes.[1] It is regarded as an extension of the principle of indifference.
Prior probabilities determined by this principle are objective in that they rely solely on the inherent characteristics of the problem, ensuring that any two individuals applying the principle to the same issue would assign identical prior probabilities. Thus, this principle is integral to the objective Bayesian interpretation of probability.
^Jaynes, Edwin T. (1968). "Prior Probabilities" (PDF). IEEE Transactions on Systems Science and Cybernetics. 4 (3): 227–241. doi:10.1109/TSSC.1968.300117. Archived (PDF) from the original on 2023-06-21. Retrieved 2023-06-30.
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form of corporatism the proponents of which claim or believe that corporate groups should form the basis of society and the state. By this principle, the...
of potentially threatening activities or substances (Cooney, 2005). Over time, there has been a gradual transformationof the precautionary principle...
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