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Authors

Archana Rajesh Meshram

Dr. Peer Javaid Ahmad

Abstract

Improving our ability to calculate birth probabilities among diverse subgroups in a population is crucial for understanding population growth dynamics. The various reproductive behaviours inherent in real-world communities are typically ignored by traditional demographic models, which generally presuppose uniformity. Using the Expectation-Maximization (EM) technique, this research presents a probabilistic framework for predicting heterogeneous birth probability. The population is modelled as a finite mixture of Bernoulli distributions, with a different birth probability for each subgroup. In order to uncover hidden demographic patterns in binary birth outcome data, the EM technique is used to estimate the parameters of the latent mixture model repeatedly. In spite of imbalances and noise, the method converges quickly and accurately in synthetic dataset trials. A real-world case study also shows how the approach may be applied to find hidden subgroups with different reproductive trends. To find the best amount of subpopulations, model selection techniques like the Bayesian Information Criterion (BIC) might be utilised. The suggested strategy increases both the accuracy of estimates and their interpretability in demographic research, according to the results. Because it provides a detailed, data-driven picture of fertility trends in different populations, this method shows potential for long-term population forecasting, resource allocation, and policy modelling.

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References

  1. Böhning, D. (2000). Computer-assisted analysis of mixtures and applications: Meta-analysis, disease mapping and others. Chapman and Hall/CRC.
  2. Caswell, H. (2001). Matrix population models: Construction, analysis, and interpretation (2nd ed.). Sinauer Associates.
  3. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–22.
  4. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). CRC Press.
  5. Ghosh, J. K., Delampady, M., & Samanta, T. (2006). An introduction to Bayesian analysis: Theory and methods. Springer.
  6. Hilborn, R., & Mangel, M. (1997). The ecological detective: Confronting models with data. Princeton University Press.
  7. Kéry, M., & Schaub, M. (2012). Bayesian population analysis using WinBUGS: A hierarchical perspective. Academic Press.
  8. King, R., Morgan, B. J. T., Gimenez, O., & Brooks, S. P. (2009). Bayesian analysis for population ecology. CRC Press.
  9. Little, R. J. A., & Rubin, D. B. (2019). Statistical analysis with missing data (3rd ed.). Wiley.
  10. McLachlan, G., & Peel, D. (2000). Finite mixture models. Wiley.
  11. Morris, W. F., & Doak, D. F. (2002). Quantitative conservation biology: Theory and practice of population viability analysis. Sinauer Associates.
  12. Newman, M. E. J. (2010). Networks: An introduction. Oxford University Press.
  13. Otto, S. P., & Day, T. (2007). A biologist's guide to mathematical modeling in ecology and evolution. Princeton University Press.
  14. Pledger, S., Pollock, K. H., & Norris, J. L. (2003). Open capture-recapture models with heterogeneity: II. Jolly–Seber model. Biometrics, 59(4), 786–794.
  15. Pollock, K. H. (2002). The use of auxiliary variables in capture–recapture modelling: An overview. Journal of Applied Statistics, 29(1–4), 85–102.
  16. Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge University Press.
  17. Royle, J. A., & Dorazio, R. M. (2008). Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities. Academic Press.
  18. Seber, G. A. F. (1982). The estimation of animal abundance and related parameters (2nd ed.). Macmillan.
  19. Turchin, P. (2003). Complex population dynamics: A theoretical/empirical synthesis. Princeton University Press.
  20. Zucchini, W., MacDonald, I. L., & Langrock, R. (2016). Hidden Markov models for time series: An introduction using R (2nd ed.). CRC Press.