On laws of large numbers in $L^2$ for supercritical branching Markov processes beyond $\lambda$-positivity

TL;DR

This paper establishes necessary and sufficient conditions for laws of large numbers in $L^2$ for a broad class of branching Markov processes, including some beyond the previously studied $\lambda$-positive systems, using probabilistic methods.

Contribution

It extends laws of large numbers in $L^2$ to $\lambda$-transient systems and introduces a probabilistic approach based on spinal decompositions and many-to-few lemmas.

Findings

Conditions for $L^2$ laws of large numbers in diverse branching processes.

Characterization of when the limit is positive on survival.

A new method for simulating quasi-stationary distributions.

Abstract

We give necessary and sufficient conditions for laws of large numbers to hold in $L^2$ for the empirical measure of a large class of branching Markov processes, including $\lambda$-positive systems but also some $\lambda$-transient ones, such as the branching Brownian motion with drift and absorption at $0$. This is a significant improvement over previous results on this matter, which had only dealt so far with $\lambda$-positive systems. Our approach is purely probabilistic and is based on spinal decompositions and many-to-few lemmas. In addition, we characterize when the limit in question is always strictly positive on the event of survival, and use this characterization to derive a simple method for simulating (quasi-)stationary distributions.