*Tid:* **16 juni 2017 kl 15.15-15.45.**
**Seminarierummet 3721**, Institutionen för
matematik, KTH, Lindstedtsvägen 25, plan 7.
Karta!
*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.
The full report (pdf)
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