Minicourse: Monte Carlo methods for spatial point processes

A spatial point process is a random collection of points in a region. They are used to model location data. For example, trees in a forest, outbreaks of disease, and bacteria in a petri dish all are spatial point processes.

In this course I'll describe the mathematical framework for creating these models, and methods for exploring these models using randomness. In particular, I'll develop discrete and continuous time birth-and-death Markov chains, Metropolis-Hastings chains, and perfect sampling methods that are used for various models. Measure theory at the level of Math 241 or Stat 205 will be assumed, some familiarity with Markov chains will be helpful.