How does high dimensionality affect Bayesian inference?

Posterior distributions become extremely peaked or diffuse, making inference unstable.

In high dimensions, likelihoods concentrate and priors become negligible unless carefully designed. Sampling methods like MCMC suffer from slow mixing due to geometry of high-dimensional posterior spaces.