BAYESIAN DEMOGRAPHY

ABS Schools

The Applied Bayesian Statistics summer school has been running since 2004. Since 2012 it is organised by

• IMATI CNR Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Milano
• Dipartimento di Scienze Statistiche Università Cattolica, Milano

Since 2014 the school is organized in cooperation with Fondazione "Alessandro Volta"

The school aims to present state-of-the-art Bayesian applications, inviting leading experts in their field.
Each year a different topic is chosen.

Past editions were devoted to: Gene Expression Genomics, Decision Modelling in Health Care, Spatial Data in Environmental and Health Sciences, Bayesian Methods and Econometrics, Bayesian Decision Problems in Biostatistics and Clinical Trials, Bayesian Methodology for Clustering, Classification and Categorical Data Analysis, Bayesian Machine Learning with Biomedical Applications, Hierarchical Modeling for Environmental Processes, Stochastic Modelling for Systems Biology, Bayesian Methods for Variable Selection with Applications to High-Dimensional Data and Applied Bayesian Nonparametrics, Modern Bayesian Methods and Computing for the Social Sciences, Bayes Big Data and the Internet, Modeling Spatial And Spatio-Temporal Data With Environmental Applications, Bayesian Statistical Modelling and Analysis in Sport.

The lecturers are Prof. Adrian Raftery and Dr. Hana Ševčíková (University of Washington, Seattle, USA).

The topic chosen for the 2019 school is
Bayesian Demography.

The instructors are leaders of the research group that developed the methods to be taught in the course (http://bayespop.csss.washington.edu).

Population projections have until recently usually been done deterministically. Recently, the United Nation Population Division adopted a probabilistic approach to project fertility, mortality and population for all countries. In this approach, the total fertility rate and female and male life expectancy at birth are projected using Bayesian hierarchical models estimated via Markov Chain Monte Carlo. They are then combined with a cohort component model, which yields probabilistic projection for any quantity of interest. The methodology is implemented in a suite of R packages, which has been used by the UN analysts to produce the most recent revision of the World Population Prospects.
This course will teach the theory and practice of Bayesian probabilistic projections. Ideas of the Bayesian hierarchical modeling for the three main components of population change, fertility, mortality and migration, will be explained. In hands-on exercises, students will become familiar with the functionality of the R packages.

By the end of the course, participants will have a basic understanding of the methods, be able to generate projections using their own data, and visualize probabilistic projections for many quantities of interest using various output formats, such as graphs, tables, maps, and pyramids. The target audience for the course includes professional demographers in government, international agencies, universities and industry, as well as advanced students in statistics and other relevant disciplines (demography, sociology, economics, anthropology, actuarial science, etc.).

• Day 1: Basic concepts of demography and review of Bayesian statistics.
• Day 2: Probabilistic projection of fertility and life expectancy.
• Day 3: Probabilistic population projection.
• Day 4: Probabilistic migration projection.
• Day 5: Subnational projections and Bayesian population reconstruction.