This paper reviews the literature on Bayesian portfolio analysis. Information about events,
macro conditions, asset pricing theories, and security-driving forces can serve as useful …

Drawing from the authors' own work and from the most recent developments in the field,
Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity …

Recently, the graphical lasso procedure has become popular in estimating Gaussian
graphical models. In this paper, we introduce a fully Bayesian treatment of graphical lasso …

Finding an unconstrained and statistically interpretable reparameterization of a covariance
matrix is still an open problem in statistics. Its solution is of central importance in covariance …

The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models
involves complex data structures and is often described as a revolutionary development. An …

An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …

This paper adopts a new approach that accounts for breaks to the parameters of return
prediction models both in the historical estimation period and at future points. Empirically …

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of
plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like …

We describe a comprehensive framework for performing Bayesian inference for Gaussian
graphical models based on the G-Wishart prior with a special focus on efficiently including …

Copula models have become one of the most widely used tools in the applied modelling of
multivariate data. Similarly, Bayesian methods are increasingly used to obtain efficient …