Nonparametric Bayesian Inference: Contributions by Jean-Marie Rolin
About this ebook
• The Mathematical structure of the Bayesian model and the main concepts (sufficiency, ancillarity, invariance...)
• Representation of the Dirichlet processes and of the associated Polya urn model and applications to nonparametric bayesian analysis.
• Contributions to duration models and to their non parametric bayesian treatment.
About the author
Jean-Pierre Florens is an influential French econometrician at Toulouse School of Economics. He is known for his research on Bayesian information, econometrics of stochastic processes, causality, frontier estimation, and inverse problems. He has written 3 books and over 100 articles. Born in Marseille in 1947, France, he completed his undergraduate studies in economics, political science, and mathematics at the Aix-Marseille University. He pursued graduate studies in mathematics at the University of Rouen. He is a fellow of the Econometric Society.
Michel Mouchart was born in Huy in 1939, Belgium. He completed undergraduate studies in Commercial Sciences at the Institut Supérieur Saint-Ignace (Antwerp) and Economics at the University of Louvain, along with a PhD in Economics. He has been an Elected Member of International Statistical and member of Institute Société Belge de Statistique. He is also a member of the Editorial Boards: Statistica, Internationale Econometric Review. His biography has 101 articles and 3 books (as author or co-author).
