Fundamental approximations -- Properties and derivations -- Multivariate densities -- Conditional densities and distribution functions -- Exponential families and tilted distributions -- Further exponential family examples and theory -- Probability computation with p* -- Probabilities with r*-type approximations -- Nuisance parameters -- Sequential saddlepoint applications -- Applications to multivariate testing -- Ratios and roots of estimating equations -- First passge and time to event distributions -- Bootstrapping in the transform domain -- Bayesian applications -- Nonnormal bases
Summary
How approximate probability calculations make complex models tractable; clear, simple explanations; real data examples
