Intellectual Capital and Knowledge Management - Strategic Management of Knowledge Resources

The Logic in Ancient Prophecy reasons why Jesus should have been chosen instead. The work interprets "Revelation," a collection of visions recorded by a writer named John. The crux of John's visions have been placed during nineteenth century America; for example, the "famous prostitute" is depicted as the 1886 unveiling of the Statue of Liberty; and the two "beasts," the historical relationship between the United States and France. The work reveals the logical relationship that these visions have with actual places and events; also, how those events affect the way we perceive freedom in our lives today.

The Logic in Ancient Prophecy reasons that in these uncertain times freedom is threatened as never before. Yet, no form of government could ever preserve or take away the more spiritual way of the rabbi Jesus-forgiveness, not vengeance-so freedom is ours, and always will be.

Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model, and the proportional hazards model.

The book begins with an outline of the similarities and differences between Bayesian and the likelihood approaches to statistics. Subsequent chapters present key techniques for using computer software to draw Monte Carlo samples from the incompletely known posterior distribution and performing the Bayesian inference calculated from these samples. Topics of coverage include:

• Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distribution
• The distributions from the one-dimensional exponential family
• Markov chains and their long-run behavior
• The Metropolis-Hastings algorithm
• Gibbs sampling algorithm and methods for speeding up convergence
• Markov chain Monte Carlo sampling

Using numerous graphs and diagrams, the author emphasizes a step-by-step approach to computational Bayesian statistics. At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, and detailed appendices in the book guide readers through the use of these software packages.