Buongiorno,
allego l'annuncio del seminario per la settimana prossima, che sarà
tenuto da Stefano Massei (EPF Lausanne).
L'appuntamento è giovedì 6 dicembre alle 10:00 presso il Dipartimento di
Informatica, Sala Seminari Ovest.
-- Leonardo Robol.
====
Speaker: Stefano Massei
Affiliation: EPF Lausanne
Time: Thursday, 6 December 2018, h. 10:00
Place: Sala Seminari Ovest, Dipartimento di Informatica.
Title: A low-rank technique for computing the quasi stationary
distribution of Galton-Watson processes
Branching processes describe the dynamics of a population of individuals
which reproduce and die independently,
according to some specific probability distributions. More precisely, we
assume that any individual has a unit lifetime,
at the end of which it might give birth to one or more offsprings
simultaneously.
This is encoded into the probability generating function P(z):=\sum p_j
z^j where p_j is the probability of generating j individuals. These kind
of processes are known in the literature as Galton-Watson processes. We
consider populations that are certain to become extinct, yet appear to
be stationary over any reasonable time scale.
More precisely, we are interested in characterizing the quasi-stationary
distribution of the process, i.e., the asymptotic distribution of the
population size, conditional on its survival.
Yaglom proved that if m:=P'(1)<1 then the quasi stationary distribution
exists and its probability generating function G(z):=\sum g_j z^j solves
the Schroeder functional equation
G(P(z))= mG(z)+1-m, z in [0,1]. (*)
We study the link between the regularity of P(z) and that of G(z) and we
propose a strategy for solving (*) in the case where P(z) and G(z) are
analytic on a disc of radius r>1.
We see that the discretization of (*) leads to a numerical method that
is capable to find arbitrary accurate approximations of the coefficients
of G(z).
Moreover, we point out the (numerical) low-rank structure that appears
in the discretized problem, and we show how to exploit it in the
proposed procedure.