Spatial point processes play a fundamental role in spatial statistics and today they are a very active area of research with many new and emerging applications. Although published works address different aspects of spatial point processes, most of the classical literature deals only with nonparametric methods, and nowhere can one find a comprehensive treatment of the theory and applications of simulation-based inference. Written by researchers at the top of the field, this book collects and unifies recent theoretical advances and examples of applications. The authors examine Markov chain Monte Carlo (MCMC) algorithms and explore one of the most important recent developments in MCMC-perfect simulation procedures.