The myths and realities of Bayesian chronological modeling revealed

We review the history of Bayesian chronological modeling in archaeology and demonstrate that there has been a surge over the past several years in American archaeological applications. Most of these applications have been performed by archaeologists who are self-taught in this method because formal training opportunities in Bayesian chronological modeling are infrequently provided. We define and address misconceptions about Bayesian chronological modeling that we have encountered in conversations with colleagues and in anonymous reviews, some of which have been expressed in the published literature. Objectivity and scientific rigor is inherent in the Bayesian chronological modeling process. Each stage of this process is described in detail, and we present examples of this process in practice. Our concluding discussion focuses on the potential that Bayesian chronological modeling has for enhancing understandings of important topics

Non-parametric Bayesian modeling of complex networks

Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model as running example we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model's fit and predictive performance. We explain how advanced non-parametric models for complex networks can be derived and point out relevant literature

Probabilities in Economic Modeling

Economic modeling assumes, for the most part, that agents are Bayesian, that is, that they entertain probabilistic beliefs, objective or subjective, regarding any event in question. We argue that the formation of such beliefs calls for a deeper examination and for explicit modeling. Models of belief formation may enhance our understanding of the probabilistic beliefs when these exist, and may also help up characterize situations in which entertaining such beliefs is neither realistic nor necessarily rational.Decision making, Bayesian, Behavioral Economics