Is uniform prior informative?

Is uniform prior informative?

The term “uninformative prior” is somewhat of a misnomer. Such a prior might also be called a not very informative prior, or an objective prior, i.e. one that’s not subjectively elicited. In this case a uniform prior of p(A) = p(B) = p(C) = 1/3 seems intuitively like the only reasonable choice.

What is proper prior distribution?

A prior distribution that integrates to 1 is a proper prior, by contrast with an improper prior which doesn’t. For example, consider estimation of the mean, μ in a normal distribution.

Which is the best description of a noninformative prior?

This might be called a weakly informative prior. (3) Prior distributions that are uniform, or nearly so, and basically allow the information from the likelihood to be interpreted probabilistically. These are noninformative priors, or maybe, in some cases, weakly informative.

Which is the best definition of a prior distribution?

This would be a traditional informative prior, which might come from a literature review or explicitly from an earlier data analysis. (2) Prior distributions that are not supplying any controversial information but are strong enough to pull the data away from inappropriate inferences that are consistent with the likelihood.

Can a noninformative prior lead to an improper posterior?

When formally combined with the data likelihood, sometimes it yields an improper posterior distribution. As for neural network models most of the standard noninformative prior construction technique will lead to improper posteriors.

Which is the simplest noninformative prior for a neural network?

Lee (2005) discussed several noninformative priors for the neural network models. The simplest noninformative prior assigns independent uniform distributions to the regression coefficients β s, input layer weights γ s, and the log of the variance. The resulting joint prior is given by