Spatial Statistics and Computational Methods (original) (raw)

This work presents a comprehensive overview of spatial statistics and computational methods through four chapters. The first chapter focuses on algorithms such as Metropolis-Hastings and Gibbs Sampler, emphasizing their relation to simulated annealing and MCMC techniques. The second chapter delves into geostatistics, discussing model-based methods, multivariate Gaussian distributions, and applications to Swiss rainfall and radiation data. The third chapter explores image analysis, differentiating between low-level and high-level tasks using models such as Markov Random Fields, with practical examples. Finally, the fourth chapter addresses spatial point processes, particularly Poisson and Log Gaussian Cox processes, using data sets related to weeds and Norwegian spruce.