Data Science Applications - Environment/Ecology
Explore data science applications in environment and ecology through this comprehensive lecture by Professor Ruth King from the University of Edinburgh. Delve into various data collection methods, including spatial, spatio-temporal, and individual-level data. Learn about hidden Markov models, state-space models, and Bayesian inference techniques. Examine real-world examples using ring-recovery and capture-recapture data, and understand the process of constructing statistical models. Discover the differences between classical and Bayesian approaches to parameter estimation and model choice. Gain insights into handling issues such as missing data and incorporating different forms of heterogeneity. Apply these concepts to practical examples in ecology and epidemiology, including analyses of capture-recapture and count data using Bayesian methods.