Population Projection using MCMC Technique in Bayesian Theory
India(2011 - 2051)
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This book is an attempt to make probabilistic projections of the population. The objective of the work was to study the applicability of the logistic growth models for the fitting of time series population data in India. The study also endeavors to make probabilistic projection of the population using MCMC tools in Bayesian setup. The popular Bayesian software WinBUGS has been applied for Bayesian analysis. There enters lot of uncertainties in the population projection and it is pertinent to quantify them in the projections. The traditional approach was to make deterministic population project...
This book is an attempt to make probabilistic projections of the population. The objective of the work was to study the applicability of the logistic growth models for the fitting of time series population data in India. The study also endeavors to make probabilistic projection of the population using MCMC tools in Bayesian setup. The popular Bayesian software WinBUGS has been applied for Bayesian analysis. There enters lot of uncertainties in the population projection and it is pertinent to quantify them in the projections. The traditional approach was to make deterministic population projections and the uncertainties in projections were presented with the help of three variants of assumptions - low, medium, and high. This study has observed that Four Parameter Logistic growth model has an ability to fit the time series data of the population of India and its provinces and it may safely be used for the population projection. Bayesian demography is a developing branch of demography and there is a huge scope of research in this field. The people interested in Bayesian demography may find some applications presented in the book useful for them.
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