In this thesis, we investigate the properties of Bayesian methods. In particular, we want to give frequentist guarantees for Bayesian methods. A Bayesian starts with specifying their apriori belief as a probability distribution, the prior distribution. The prior is their inherently subjective beliefs. After a Bayesian has specified their prior, they collect data and compute the posterior distribution. For a Bayesian, this posterior distribution encodes their new beliefs on the world. However, this prior was subjective. Thus the posterior is also subjective. So we can wonder, will this posterior distribution give a better representation of reality? Will it be more accurate? The posterior distribution quantifies a subjective belief of uncertainty. How reliable is this quantification of uncertainty? These questions lie at the foundation of this thesis. They have been answered for certain classes of prior distributions. However, they have not been fully answered for all distributions in use. In this thesis, in the introduction, we explain the foundational statistical theory to study these questions. In particular, we show how to apply Schwartz theorem and the Bernstein-von Mises theorems to study posterior distributions. We then turn to novel research.....@en

The Pitman-Yor process is a random probability distribution, that can be used as a prior distribution in a nonparametric Bayesian analy-sis. The process is of species sampling type and generates discrete distribu-tions, which yield of the order n^σ different values (“species”) in a random sample of size n, ifthetypeσ is positive. Thus this type parameter can be set to target true distributions of various levels of discreteness, making the Pitman-Yor process an interesting prior in this case. It was previously shown that the resulting posterior distribution is consistent if and only if the true distribution of the data is discrete. In this paper we derive the dis-tributional limit of the posterior distribution, in the form of a (corrected) Bernstein-von Mises theorem, which previously was known only in the con-tinuous, inconsistent case. It turns out that the Pitman-Yor posterior distribution has good behaviour if the true distribution of the data is discrete with atoms that decrease not too slowly. Credible sets derived from the posterior distribution provide valid frequentist confidence sets in this case. For a general discrete distribution, the posterior distribution, although con-sistent, may contain a bias which does not converge to zero at the^√n rate and invalidates posterior inference. We propose a bias correction that solves this problem. We also consider the effect of estimating the type parameter from the data, both by empirical Bayes and full Bayes methods. In a small simulation study we illustrate that without bias correction the coverage of credible sets can be arbitrarily low, also for some discrete distributions.

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