Galton-Watson process: a non parametric prior for the offspring distribution

Cannas, Massimo
;
Guindani, Michele;Piras, Nicola
2023-01-01

Abstract

In this article we propose a non parametric prior for the probabilities of the Galton- Watson process based on the Dirichlet Process. After recalling the main properties of the Galton-Watson process and presenting the estimation methods already present in the lit- erature, such as maximum likelihood and Bayesian conjugate analysis, we define the new prior by pointing out how it is more general than the Dirichlet prior used in the conjugate analysis, which is a special case of our extension. Finally, we show the results of a simula- tion study illustrating how our analysis leads to a more accurate classification of the process.
2023
Inglese
Statistical learning, sustainability and policy evaluation
9788891935618
Pearson
Francesco Maria Chelli, Mariateresa Ciommi, Salvatore Ingrassia, Francesca Mariani, Maria Cristina Recchioni
328
333
6
SEAS IN: statistical learning, sustainability and policy evaluation
Contributo
Esperti anonimi
21-23 giugno 2023
Ancona
internazionale
scientifica
Galton-Watson process; Dirichlet process; Bayesian inference; offspring distribution
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Cannas, Massimo; Guindani, Michele; Piras, Nicola
273
3
4.1 Contributo in Atti di convegno
reserved
info:eu-repo/semantics/conferencePaper
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