I. M. del Puerto, M. González Velasco, C. Gutiérrez Pérez, R. Martínez Quintana
Branching Processes provide appropriate mathematical models for description of the probabilistic evolution of systems whose components (cells, particles, individuals in general), after a certain life period, reproduce and die. In particular, a controlled branching process (CBP) is a generalization of the classical Galton-Watson branching process, and, in the terminology of population dynamics, is used to describe the evolution of populations in which a control of the population size at each generation is needed. In this work, we deal with the problem of estimating the offspring parameters for a CBP assuming that the only observable data are the total number of individuals in each generation.We tackle the problem from a Bayesian perspective using MCMC and ABC methodologies. The results are illustrated with simulated data examples. Acknowledgement: This research is supported by the Ministerio de Ciencia e Innovación, Junta de Extremadura and FEDER through the grants MTM2009-13248 and GR10118.
Palabras clave: branching processes, Bayesian inference, Markov Chain Monte Carlo Methods, approximate Bayesian computation
Programado
MD1 Métodos bayesianos 2
17 de abril de 2012 15:30
Salón Madrid
