This introduction to Bayesian inference places special emphasis on applications. All basic concepts are presented: Bayes' theorem, prior density functions, point estimation, confidence region, hypothesis testing and predictive analysis. In addition, Monte Carlo methods are discussed since the applications mostly rely on the numerical integration of the posterior distribution. Furthermore, Bayesian inference in the linear model, nonlinear model, mixed model and in the linear model with unknown variance and covariance components is considered. Solutions are supplied for the classification, for the posterior analysis based on distributions of robust maximum likelihood type estimates, and for the reconstruction of digital images.
There are currently no reviews for this product. Be the first to review this product!
Your orders support book donation projects
Vastly superior to the Amazon offering. Recommended unreservedly.
Search and browse over 110,000 wildlife and science products
Multi-currency. Secure worldwide shipping
Wildlife, science and conservation since 1985