Optimal prior for Bayesian inference in a constrained parameter space

Understand

Bayesian parameter inference depends on a choice of prior probability distribution for the parameters in question.

• The prior which makes the posterior distribution maximally sensitive to data is called the Jeffreys prior, and it is completely determined by the response of the likelihood to changes in parameters.
• Under the assumption that the likelihood is a Gaussian distribution, the Jeffreys prior is a constant, i.e.
• flat.