In many problems, complex non-Gaussian and/or nonlinear models are required to accurately describe a physical system of interest. In such cases, Monte Carlo algorithms are remarkably flexible and extremely powerful approaches to solve such inference problems. However, in the presence of a...

In empirical economics and finance, inference is often based on previously-used data. We show that valid Bayesian inference is noninformative for previously-used data. Given the prevalence of previously-used data and the success of ``Bayesian" methods in empirical economics and finance, this is...

A 2017 paper by Harris argues that financial returns are a mixture distribution and that the dominant distribution is the truncated Cauchy distribution. Both the mixture nature and the poor properties of the truncated Cauchy distribution functionally exclude both the Frequentist and the...

mass 1 concentrated on the true process, provided that the prior probability measurehas full support and the true process is irreducible. Second, I extend this result to thecase in which k is unbounded (but finite), which requires that the Bayesian decisionmaker(DM) construct a prior on an...

We study how changes to the informativeness of signals in Bayesian games and single-agent decision problems affect the distribution of equilibrium actions. A more precise private signal about an unknown state of the world leads to an mean-preserving spread of an agent's beliefs. Focusing on...

We consider Bayesian inference by importance sampling when the likelihood is analytically intractable but can be unbiasedly estimated. We refer to this procedure as importance sampling squared (IS2), as we can often estimate the likelihood itself by importance sampling. We provide a formal...