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Probability II / Branching Processes

Overview

The Galton-Watson branching process is a second worked instance of a Markov chain — population size is itself a Markov chain on {0,1,2,…}\{0,1,2,\ldots\} — chosen because it introduces a genuinely new tool, the probability generating function, needed precisely because the state space is infinite and the matrix methods of Discrete-Time Markov Chains do not directly apply. The topic is deliberately short: two lessons are enough to state Galton's 1873 extinction question, answer it completely via a generating-function fixed-point argument, and classify the three qualitatively different regimes the answer produces.

By the end of this topic you should be able to:

  • set up a Galton-Watson process and compute its generating function's composition rule;
  • state and apply the extinction probability theorem, including the geometric-offspring closed form;
  • classify a process as subcritical, critical, or supercritical from its offspring mean, and describe the qualitatively different behaviour of each regime.

In this lesson

  1. Contents
  2. The big picture

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