Improper prior

An improper prior is a nonnegative prior kernel with infinite total mass. It can still produce a proper posterior after multiplication by the likelihood and normalization.

The Haldane prior has formal beta distribution parameters $(0,0)$. It is an improper prior, with infinite mass at both endpoints of $(0,1)$. For a binomial distribution likelihood with $s$ successes and $f$ failures, its posterior is $\mathrm{Beta}(s,f)$ only when $s,f>0$. A density flat in the log odds induces this kernel by the Jacobian determinant $d\log [p/(1-p)]/dp=1/[p(1-p)]$.

The measure $d\sigma /\sigma$ is invariant under multiplication by every positive constant. In the logarithmic coordinate it is Lebesgue measure. It cannot be normalized over $(0,\infty )$, although a bounded truncation is a log-uniform distribution.

A random-effect model with positive marginal likelihood at scale zero cannot yield a proper posterior distribution under a prior proportional to the reciprocal scale near zero. On a compact set of the other parameters, continuity and positivity give a positive likelihood lower bound, while ${\int }_0^{\epsilon }d\lambda /\lambda =\infty$. Individual full conditional distributions can still be proper, so their existence alone does not justify a Gibbs sampler with a probability distribution as its joint target.

A posterior kernel $L(\theta )\pi (\theta )$ defines a posterior distribution exactly when its integral is finite and nonzero. Proper full conditional densities do not suffice. For example, if a known positive measurement variance makes a marginal normal likelihood approach a strictly positive limit as a latent variance $v$ tends to zero, the log-flat prior $dv/v$ gives infinite posterior mass at that boundary. The same problem occurs for a latent exponential scale $\tau$ with $d\tau /\tau$ when the observed likelihood has a positive limit as the exponential contribution vanishes.

If a model configuration leaves a parameter absent from its likelihood function, giving that parameter an independent flat improper prior makes the posterior distribution normalization infinite whenever the configuration has positive prior distribution mass. Integrate first over that parameter: a positive constant likelihood function factor is repeated over all of $\mathbb{R}$. This occurs for the second segment mean of a Gaussian change-point detection model when the proposed split is after the final observation. There is no posterior distribution or valid Gibbs sampler for that joint kernel. A proper prior distribution on the unused parameter is necessary if the no-change configuration is retained.