Bayesian Statistics from Methods to Models and Applications

On Bayesian based adaptive confidence sets for linear functionals.- A new finite approximation for the NGG mixture model: an application to density estimation.- Distributed Estimation of Mixture Models.- Bayesian Survival Model based on Moment Characterization.- Identifying the Infectious Period Distribution for Stochastic Epidemic Models Using the Posterior Predictive Check.- A subordinated stochastic process model.- Jeffreys priors for mixture estimation.- Bayesian Variable Selection for Generalized Linear Models Using the Power-Conditional-Expected-Posterior Prior.- A new strategy for testing cosmology with simulations.- Mixture Model for Filtering Firms' Profit Rates.- Bayesian Estimation of the Aortic Stiffness based on Non-Invasive Computed Tomography Images.- Formal and Heuristic Model Averaging Methods for Predicting the US Unemployment Rate.- Bayesian Filtering for Thermal Conductivity Estimation given Temperature Observations.- Application of Interweaving in DLMs to an Exchange and Specialization Experiment.

Sylvia Frühwirth-Schnatter is a Professor of Applied Statistics and Econometrics at the Department of Finance, Accounting, and Statistics at the WU Vienna University of Economics and Business, Austria. She received her PhD in Mathematics from the Vienna University of Technology in 1988. She has published in many leading journals in applied statistics and econometrics on topics such as Bayesian inference, finite mixture models, Markov switching models, state space models, and their application in economics, finance, and business. In 2014, she became elected member of the Austrian Academy of Science.

Angela Bitto holds a Masters in Mathematics and is currently working on her PhD in Statistics at the Vienna University of Technology. Her research focuses on the Bayesian estimation of sparse time-varying parameter models. Prior to joining the Institute of Statistics and Mathematics at the WU Vienna University of Economics and Business, she worked as a research analyst for the European Central Bank.

Gregor Kastner is an Assistant Professor at the WU Vienna University of Economics and Business and a Lecturer at the University of Applied Sciences in Wiener Neustadt, Austria. He holds Masters in Mathematics, Computer Science, Informatics Management, and Physical Education; in 2014 he received his PhD in Mathematics. Gregor researches the Bayesian modeling of economic time series, in particular the efficient estimation of univariate and high-dimensional stochastic volatility models. His work has been published in leading journals in computational statistics and computer software.

Alexandra Posekany is an Assistant Professor at the Institute of Statistics and Mathematics, WU Vienna University of Economics and Business, Austria. She holds a PhD in Mathematics from the Vienna University of Technology. Her research includes applications of Bayesian analysis in computational biology and econometrics, as well as the development of algorithms