Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems.
This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.
... Parametric Sensitivity Analysis Using Large-Sample Approximate Bayesian Posterior Distributions, Decision Analysis 3(4), 208–219. Hazen, G. and Sounderpandian, J.: 1999, Lottery Acquisition versus Information Acquisition: Prices and ...
... Bayesian inference and Bayesian sensitivity analysis understand robustness and insensitivity mostly as desirable ... distributions ( with a support of size k < ∞ ) the computation of posterior credal sets is covered in Chapter 16 ...
This volume, therefore, presents an overview of the current state of robust Bayesian methods and their applications and identifies topics of further in terest in the area.
... Bayesian methods In general , likelihood - based methods for handling missing data assume a parametric model for the ... large - sample samples , the MLE has an approximate normal distribution with covariance matrix given by the ...
After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.
... Bayesian computation It is clear from Example 6.1 that the derivation of posterior quantities of interest, such as HPD regions, posterior moments, marginal posterior distributions ... large-sample theory. In Chapter 3 it was stated that ...
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a ...
... large-sample distributions for a specific estimator or inference procedure under a single value of the parameter, we approximate ... analysis has played an important role in developing asymptotic optimality theory for point estimators and ...