Salvation or Annihilation? A strange artifact has been discovered on a distant planet, an artifact that may be the key to humanity's salvation. For we at war with the Fallers, an alien race bent on nothing short of genocide, and this is a war we are losing. The artifact is not only a powerful weapon, but possibly the rosetta stone to a lost superscience . . . a superscience that the Fallers may have already decoded. Or it may be a doomsday machine that could destroy the very fabric of space. At the Publisher's request, this title is being sold without Digital Rights Management Software (DRM) applied.

Humankind has expanded out into interstellar space using star gates-technological remnants left behind by an ancient, long-vanished race. But the technology comes with a price. Among the stars, humanity encountered the Fallers, a strange alien race bent on nothing short of genocide. It's all-out war, and humanity is losing. In this fragile situation, a new planet is discovered, inhabited by a pre-industrial race who experience "shared reality"-they're literally compelled to share the same worldview. A team of human scientists is dispatched-but what they don't know is that their mission of first contact is actually a covert military operation. For one of the planet's moons is really a huge mysterious artifact of the same origin as the star gates . . . and it just may be the key to winning the war. At the Publisher's request, this title is being sold without Digital Rights Management Software (DRM) applied.

Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.

Comprehensive Remote Sensing, Nine Volume Set covers all aspects of the topic, with each volume edited by well-known scientists and contributed to by frontier researchers. It is a comprehensive resource that will benefit both students and researchers who want to further their understanding in this discipline. The field of remote sensing has quadrupled in size in the past two decades, and increasingly draws in individuals working in a diverse set of disciplines ranging from geographers, oceanographers, and meteorologists, to physicists and computer scientists. Researchers from a variety of backgrounds are now accessing remote sensing data, creating an urgent need for a one-stop reference work that can comprehensively document the development of remote sensing, from the basic principles, modeling and practical algorithms, to various applications. Fully comprehensive coverage of this rapidly growing discipline, giving readers a detailed overview of all aspects of Remote Sensing principles and applications Contains ‘Layered content’, with each article beginning with the basics and then moving on to more complex concepts Ideal for advanced undergraduates and academic researchers Includes case studies that illustrate the practical application of remote sensing principles, further enhancing understanding

This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications. Introduction to Probability covers the material precisely, while avoiding excessive technical details. After introducing the basic vocabulary of randomness, including events, probabilities, and random variables, the text offers the reader a first glimpse of the major theorems of the subject: the law of large numbers and the central limit theorem. The important probability distributions are introduced organically as they arise from applications. The discrete and continuous sides of probability are treated together to emphasize their similarities. Intended for students with a calculus background, the text teaches not only the nuts and bolts of probability theory and how to solve specific problems, but also why the methods of solution work.

Statistically Speaking is a book of quotations. It brings together the best expressed thoughts that are especially illuminating and pertinent to the disciplines of probability and statistics. The book is an aid for the individual who loves to quote – and to quote correctly.