Description
- R code implementing algorithms to fit Bayesian models using real and simulated data examples.
- A comprehensive review of statistical models commonly used in ecological and environmental science.
- Overview of Bayesian computational methods such as importance sampling, MCMC, and HMC.
- Derivations of the necessary components to construct statistical algorithms from scratch.
Table of Contents
Background. Bayesian Models. Numerical Integration. Monte Carlo. Markov Chain Monte Carlo. Importance Sampling. Basic Models and Concepts. Bernoulli 窶� Beta. Normal-Normal. Normal-Inverse Gamma. Normal-Normal-Inverse Gamma. Intermediate Models and Concepts. Mixture Models. Linear Regression. Posterior Prediction. Model Comparison. Regularization. Bayesian Model Averaging. Time Series Models. Spatial Models. Advanced Models and Concepts. Quantile Regression. Hierarchical Models.Binary Regression. Count Data Regression. Zero-Inflated Models. Occupancy Models. Abundance Models. Expert Models and Concepts. Integrated Population Models. Spatial Occupancy Models. Spatial Capture-Recapture Models. Spatio-Temporal Models. Hamiltonian Monte Carlo.
