Physically Based Rendering, Fourth Edition: From Theory to Implementation (4版)

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【簡介】 A comprehensive update of the leading-edge computer graphics textbook that sets the standard for physically-based rendering in the industry and the field, with new material on GPU ray tracing.Photorealistic computer graphics are ubiquitous in today’s world, widely used in movies and video games as well as product design and architecture. Physically-based approaches to rendering, where an accurate modeling of the physics of light scattering is at the heart of image synthesis, offer both visual realism and predictability. Now in a comprehensively updated new edition, this best-selling computer graphics textbook sets the standard for physically-based rendering in the industry and the field. Physically Based Rendering describes both the mathematical theory behind a modern photorealistic rendering system as well as its practical implementation. A method known as literate programming combines human-readable documentation and source code into a single reference that is specifically designed to aid comprehension. The book’s leading-edge algorithms, software, and ideas--including new material on GPU ray tracing--equip the reader to design and employ a full-featured rendering system capable of creating stunning imagery. This essential text represents the future of real-time graphics. Detailed and rigorous but accessible approach guides readers all the way from theory to practical software implementationFourth edition features new chapter on GPU ray tracing essential for game developersThe premier reference for professionals learning about and working in the fieldWon its authors a 2014 Academy Award for Scientific and Technical Achievement Includes a companion site complete with source code

Latent Structure and Causality: Inference from Data (1版)

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【簡介】 Inferring latent structure and causality is crucial for understanding underlying patterns and relationships hidden in the data. This book covers selected models for latent structures and causal networks and inference methods for these models.After an introduction to the EM algorithm on incomplete data, the book provides a detailed coverage of a few widely used latent structure models, including mixture models, hidden Markov models, and stochastic block models. EM and variation EM algorithms are developed for parameter estimation under these models, with comparison to their Bayesian inference counterparts. We make further extensions of these models to related problems, such as clustering, motif discovery, Kalman filtering, and exchangeable random graphs. Conditional independence structures are utilized to infer the latent structures in the above models, which can be represented graphically. This notion generalizes naturally to the second part on graphical models that use graph separation to encode conditional independence. We cover a variety of graphical models, including undirected graphs, directed acyclic graphs (DAGs), chain graphs, and acyclic directed mixed graphs (ADMGs), and various Markov properties for these models. Recent methods that learn the structure of a graphical model from data are reviewed and discussed. In particular, DAGs and Bayesian networks are an important class of mathematical models for causality. After an introduction to causal inference with DAGs and structural equation models, we provide a detailed review of recent research on causal discovery via structure learning of graphs. Finally, we briefly introduce the causal bandit problem with sequential intervention.