Bayesian Scientific Computing

• Inverse problems and subjective computing.- Linear algebra.- Continuous and discrete multivariate distributions.- Introduction to sampling.- The praise of ignorance: randomness as lack of certainty.- Enter subject: Construction of priors.- Posterior densities, ill-conditioning, and classical regularization.- Conditional Gaussian densities.- Iterative linear solvers and priorconditioners.- Hierarchical models and Bayesian sparsity.- Sampling: the real thing.- Dynamic methods and learning from the past.- Bayesian filtering and Gaussian densities.-

“The book is presented in a logical order, with good writing style. One of the notable features is that the topics are well illustrated with graphs and figures.” (John Masson Noble, Mathematical Reviews, September, 2024)