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Stochastic Models, Estimation, and Control
This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.
Mathematics for Machine Learning
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
Bayesian Filtering and Smoothing
A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Autonomous Robot Vehicles
Autonomous robot vehicles are vehicles capable of intelligent motion and action without requiring either a guide or teleoperator control. The recent surge of interest in this subject will grow even grow further as their potential applications increase. Autonomous vehicles are currently being studied for use as reconnaissance/exploratory vehicles for planetary exploration, undersea, land and air environments, remote repair and maintenance, material handling systems for offices and factories, and even intelligent wheelchairs for the disabled. This reference is the first to deal directly with the unique and fundamental problems and recent progress associated with autonomous vehicles. The editors have assembled and combined significant material from a multitude of sources, and, in effect, now conviniently provide a coherent organization to a previously scattered and ill-defined field.
Kalman Filtering
The definitive textbook and professional reference on Kalman Filtering – fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.
Experiencing Architecture, second edition
A classic examination of superb design through the centuries. Widely regarded as a classic in the field, Experiencing Architecture explores the history and promise of good design. Generously illustrated with historical examples of designing excellence—ranging from teacups, riding boots, and golf balls to the villas of Palladio and the fish-feeding pavilion of Beijing's Winter Palace—Rasmussen's accessible guide invites us to appreciate architecture not only as a profession, but as an art that shapes everyday experience. In the past, Rasmussen argues, architecture was not just an individual pursuit, but a community undertaking. Dwellings were built with a natural feeling for place, materi...
Applied Optimal Estimation
This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation. Even so, theoretical and mathematical concepts are introduced and developed sufficiently to make the book a self-contained source of instruction for readers without prior knowledge of the basic principles of the field. The work is the product of the technical staff of The Analytic Sciences Corporation (TASC), an organization whose success has resulted largely from its applications of optimal estimation techniques to a wide variety of real situations involving large-scale systems. Arthur Gelb writes in the For...
Efficient Reinforcement Learning Using Gaussian Processes
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.
Bad Buying
"A fascinating litany of the mistakes that can happen when buyers get it wrong" - Luke Johnson, The Sunday Times "Packed full with amazing examples' Jeremy Vine, BBC Radio 2 "Colossal, costly disasters could be averted if those holding the purse strings read this book. - The Times In this hilarious, fascinating and insightful expose, industry insider Peter Smith reveals the massive blunders and dodgy dealings taking place around the world as private companies and public sector bodies buy goods and services. A recent report showed that over 90% of procurement projects fail. So, why are so many billions wasted on ineptitude, mismanagement and, in some cases, fraud? By turns an entertaining acc...
Linear Systems Theory
A fully updated textbook on linear systems theory Linear systems theory is the cornerstone of control theory and a well-established discipline that focuses on linear differential equations from the perspective of control and estimation. This updated second edition of Linear Systems Theory covers the subject's key topics in a unique lecture-style format, making the book easy to use for instructors and students. João Hespanha looks at system representation, stability, controllability and state feedback, observability and state estimation, and realization theory. He provides the background for advanced modern control design techniques and feedback linearization and examines advanced foundation...
