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The simplest and oldest rule for determining a non-informative prior is the principle of indifferencewhich assigns equal probabilities to all possibilities. In parameter estimation problems, the use of an uninformative prior typically yields results which are not too different from conventional statistical analysis, as the likelihood function often yields more information than the uninformative prior.
Some attempts have been made at finding a priori probabilitiesi.
Perhaps the strongest arguments for objective Bayesianism were given by Edwin T. Jaynesbased mainly on the consequences of symmetries and on the principle of maximum entropy.