David Poole

A Modern Introduction

Linear Algebra

In this innovative new Linear Algebra text, award-winning educator David Poole covers vectors and vector geometry first to enable students to visualize the mathematics while they are doing matrix operations. Rather than merely doing the calculations with no understanding of the mathematics, students will be able to visualize and understand the meaning of the calculations. By seeing the mathematics and understanding the underlying geometry, students will develop mathematical maturity and learn to think abstractly.

Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.

An Introduction to Lifted Probabilistic Inference

Randy Goebel

Alan K. Mackworth

David I. Poole

David Lynton Poole

RANDY AUTOR GOEBEL

Alan Mackworth

Provides an integrated introduction to artificial intelligence. Develops AI representation schemes and describes their uses for diverse applications, from autonomous robots to diagnostic assistants to infobots. DLC: Artificial intelligence.

A Logical Approach

Computational Intelligence

"The book presents an introduction to, and an authoritative guide, for anyone interested in the problem of probabilistic inference in the presence of symmetries/structured models"--

Tim Richmond was, fellow NASCAR driver Kyle Petty said, "a stranger in time." In one regard, the flashy, flamboyant driver from Ashland, Ohio, was years ahead of the trends in a sport that would soon enjoy explosive growth in popularity. Women who were NASCAR fans loved him¬¬¬—and so did their husbands and boyfriends. Richmond believed he could use his stardom in racing as a springboard to a second career as an actor, and he had the Hollywood good looks to make that a realistic dream. At the same time, Richmond was also a throwback. He pushed his race cars hard, too hard at times, driving every lap like he was hauling moonshine through the mountains of the Carolinas with a revenuer on his rear bumper. Those who saw him drive still compare him to veterans like Curtis Turner and Joe Weatherly, who ran as hard off the track as they did off of it. In the early 1980s, however, Richmond stood out. He was not from the South; he had not grown up slinging a stock car through the dirt on red-clay ovals. He had, in fact, never raced at all until he was twenty-one. And just ten years later, after making a splash in the Indianapolis 500 as a rookie, he was emerging as one of the brightest stars and greatest talents in NASCAR's Winston Cup Series. Richmond's star was bright, but its light went out too soon. As he neared stock car racing's zenith, Richmond's life took a tragic turn. A man who thrived on the affection he felt from those who enjoyed watching him compete spent his final months almost completely shut off from that world. Tim Richmond: The Fast Life and Remarkable Times of NASCAR's Top Gun tells the memorable story of a born racer and how he raced headlong through life with the throttle wide open and his wheels burning rubber at almost every turn.

The Fast Life and Remarkable Times of NASCAR's Top Gun

Tim Richmond

Kristian Kersting

Sriraam Natarajan

Luc De Raedt

An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

Logic, Probability, and Computation

Statistical Relational Artificial Intelligence

David Poole's innovative LINEAR ALGEBRA: A MODERN INTRODUCTION, 4e emphasizes a vectors approach and better prepares students to make the transition from computational to theoretical mathematics. Balancing theory and applications, the book is written in a conversational style and combines a traditional presentation with a focus on student-centered learning. Theoretical, computational, and applied topics are presented in a flexible yet integrated way. Stressing geometric understanding before computational techniques, vectors and vector geometry are introduced early to help students visualize concepts and develop mathematical maturity for abstract thinking. Additionally, the book includes ample applications drawn from a variety of disciplines, which reinforce the fact that linear algebra is a valuable tool for modeling real-life problems. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Linear Algebra: A Modern Introduction

David Poole's innovative book emphasizes vectors and geometric intuition from the start and better prepares students to make the transition from the computational aspects of the course to the theoretical. Poole covers vectors and vector geometry first to enable students to visualize the mathematics while they are doing matrix operations. With a concrete understanding of vector geometry, students are able to visualize and understand the meaning of the calculations that they will encounter. By seeing the mathematics and understanding the underlying geometry, students develop mathematical maturity and can think abstractly when they reach vector spaces. Throughout the text, Poole's direct conversational writing style connects with students, and an abundant selection of applications from a broad range of disciplines clearly demonstrates the relevance of linear algebra.

The memorable story of former racing star Tim Richmond is told, detailing how his life took a tragic turn as he neared stockcar racing's zenith as one of its brightest stars.

David Poole

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