Tid: 16 juni 2017 kl 15.15-15.45.

Föredragshållare: Simon Wallin

Title: Small cohort population forecasting via Bayesian learning

Abstract: A set of distributional assumptions regarding the demographic processes of birth, death, emigration and immigration have been assembled to form a probabilistic model framework of population dynamics. This framework is summarized as a Bayesian network and Bayesian inference techniques are exploited to infer the posterior distributions of the model parameters from observed data. The birth, death and emigration processes are modeled using a hierarchical beta-binomial model from which the inference of the posterior parameter distribution is analytically tractable. The immigration process is represented with a Poisson-type regression model where the posterior distributions of the parameters have to be estimated numerically. This thesis suggests an implementation of the Metropolis-Hasting algorithm for this task. Classification of incomings into subpopulations of age and gender is subsequently made using the Dirichlet-multinomial hierarchic model, for which parameter inference is analytically tractable. This model framework is used to generate forecasts of demographic data, which can be validated using the observed outcomes. A key component of the Bayesian model framework used is that it estimates the full posterior distributions of demographic data, taking into account the full amount of uncertainty when forecasting population growths.

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