Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing

Inverse problems and subjective computing.- Basic problem of statistical inference.- The praise of ignorance: randomness as lack of information.- Basic problem in numerical linear algebra.- Sampling: first encounter.- Statistically inspired preconditioners.- Conditional Gaussian densities and predictive envelopes.- More applications of the Gaussian conditioning.- Sampling: the real thing.- Wrapping up: hypermodels, dynamic priorconditioners and Bayesian learning.