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Author : Roger Hagafors
Publisher :
ISBN 13 :
Total Pages : 52 pages
Book Rating : 4.:/5 (319 download)
Download or read book Learning of Probabilistic Inference Tasks written by Roger Hagafors and published by . This book was released on 1981 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Daphne Koller
Publisher : MIT Press
ISBN 13 : 0262258358
Total Pages : 1270 pages
Book Rating : 4.2/5 (622 download)
Download or read book Probabilistic Graphical Models written by Daphne Koller and published by MIT Press. This book was released on 2009-07-31 with total page 1270 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Author : Fabrizio Riguzzi
Publisher : CRC Press
ISBN 13 : 1000923215
Total Pages : 548 pages
Book Rating : 4.0/5 (9 download)
Download or read book Foundations of Probabilistic Logic Programming written by Fabrizio Riguzzi and published by CRC Press. This book was released on 2023-07-07 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols was placed on a sound footing. Probabilistic Answer Set Programming gained a lot of interest together with the studies on the complexity of inference. Algorithms for solving the MPE and MAP tasks are now available. Inference for hybrid programs has changed dramatically with the introduction of Weighted Model Integration. With respect to learning, the first approaches for neuro-symbolic integration have appeared together with algorithms for learning the structure for hybrid programs. Moreover, given the cost of learning PLPs, various works proposed language restrictions to speed up learning and improve its scaling.
Author : Laura Isabel Galindez Olascoaga
Publisher : Springer Nature
ISBN 13 : 3030740420
Total Pages : 163 pages
Book Rating : 4.0/5 (37 download)
Download or read book Hardware-Aware Probabilistic Machine Learning Models written by Laura Isabel Galindez Olascoaga and published by Springer Nature. This book was released on 2021-05-19 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover. The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.
Author : Avi Pfeffer
Publisher : Simon and Schuster
ISBN 13 : 1638352372
Total Pages : 650 pages
Book Rating : 4.6/5 (383 download)
Download or read book Practical Probabilistic Programming written by Avi Pfeffer and published by Simon and Schuster. This book was released on 2016-03-29 with total page 650 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you'll learn how to use the PP paradigm to model application domains and then express those probabilistic models in code. Although PP can seem abstract, in this book you'll immediately work on practical examples, like using the Figaro language to build a spam filter and applying Bayesian and Markov networks, to diagnose computer system data problems and recover digital images. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The data you accumulate about your customers, products, and website users can help you not only to interpret your past, it can also help you predict your future! Probabilistic programming uses code to draw probabilistic inferences from data. By applying specialized algorithms, your programs assign degrees of probability to conclusions. This means you can forecast future events like sales trends, computer system failures, experimental outcomes, and many other critical concerns. About the Book Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In this book, you’ll immediately work on practical examples like building a spam filter, diagnosing computer system data problems, and recovering digital images. You’ll discover probabilistic inference, where algorithms help make extended predictions about issues like social media usage. Along the way, you’ll learn to use functional-style programming for text analysis, object-oriented models to predict social phenomena like the spread of tweets, and open universe models to gauge real-life social media usage. The book also has chapters on how probabilistic models can help in decision making and modeling of dynamic systems. What's Inside Introduction to probabilistic modeling Writing probabilistic programs in Figaro Building Bayesian networks Predicting product lifecycles Decision-making algorithms About the Reader This book assumes no prior exposure to probabilistic programming. Knowledge of Scala is helpful. About the Author Avi Pfeffer is the principal developer of the Figaro language for probabilistic programming. Table of Contents PART 1 INTRODUCING PROBABILISTIC PROGRAMMING AND FIGARO Probabilistic programming in a nutshell A quick Figaro tutorial Creating a probabilistic programming application PART 2 WRITING PROBABILISTIC PROGRAMS Probabilistic models and probabilistic programs Modeling dependencies with Bayesian and Markov networks Using Scala and Figaro collections to build up models Object-oriented probabilistic modeling Modeling dynamic systems PART 3 INFERENCE The three rules of probabilistic inference Factored inference algorithms Sampling algorithms Solving other inference tasks Dynamic reasoning and parameter learning
Author : David J. C. MacKay
Publisher : Cambridge University Press
ISBN 13 : 9780521642989
Total Pages : 694 pages
Book Rating : 4.6/5 (429 download)
Download or read book Information Theory, Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Author : Guy Van den Broeck
Publisher : MIT Press
ISBN 13 : 0262542595
Total Pages : 455 pages
Book Rating : 4.2/5 (625 download)
Download or read book An Introduction to Lifted Probabilistic Inference written by Guy Van den Broeck and published by MIT Press. This book was released on 2021-08-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Kevin P. Murphy
Publisher : MIT Press
ISBN 13 : 0262018020
Total Pages : 1102 pages
Book Rating : 4.2/5 (62 download)
Download or read book Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2012-08-24 with total page 1102 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Author : D Damos
Publisher : CRC Press
ISBN 13 : 1000124533
Total Pages : 482 pages
Book Rating : 4.0/5 (1 download)
Download or read book Multiple Task Performance written by D Damos and published by CRC Press. This book was released on 2020-07-24 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with theories of multiple-task performance and focuses on learning and performance. It is primarily for professionals in human factors, psychology, or engineering who are interested in multiple-task performance but have no formal training in the area.
Author : Cameron Davidson-Pilon
Publisher : Addison-Wesley Professional
ISBN 13 : 0133902927
Total Pages : 551 pages
Book Rating : 4.1/5 (339 download)
Download or read book Bayesian Methods for Hackers written by Cameron Davidson-Pilon and published by Addison-Wesley Professional. This book was released on 2015-09-30 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes • Learning the Bayesian “state of mind” and its practical implications • Understanding how computers perform Bayesian inference • Using the PyMC Python library to program Bayesian analyses • Building and debugging models with PyMC • Testing your model’s “goodness of fit” • Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works • Leveraging the power of the “Law of Large Numbers” • Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes • Selecting appropriate priors and understanding how their influence changes with dataset size • Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough • Using Bayesian inference to improve A/B testing • Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.
Author : Lars-Erik Warg
Publisher :
ISBN 13 :
Total Pages : 62 pages
Book Rating : 4.:/5 (35 download)
Download or read book The Effect of Task Content on Performance in Probabilistic Inference Tasks written by Lars-Erik Warg and published by . This book was released on 1983 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Kevin P. Murphy
Publisher : MIT Press
ISBN 13 : 0262369303
Total Pages : 858 pages
Book Rating : 4.2/5 (623 download)
Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Author : N. John Castellan
Publisher : Psychology Press
ISBN 13 : 1134767978
Total Pages : 315 pages
Book Rating : 4.1/5 (347 download)
Download or read book Individual and Group Decision Making written by N. John Castellan and published by Psychology Press. This book was released on 2013-02-01 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: The idea for this volume took root during a recent annual convention of the American Psychological Association. The contributors share a common vision of research in their particular area and have had an opportunity to debate and clarify their ideas. Taken as a whole, the fifteen chapters provide an exciting perspective of the field and form a basic set of readings for courses on individual and group decision making in a variety of disciplines. The coverage from basic laboratory research to complex applied group decision processes should challenge researchers and students to pursue the field of decision making as enthusiastic scientists and practitioners.
Author : Judea Pearl
Publisher : Elsevier
ISBN 13 : 0080514898
Total Pages : 573 pages
Book Rating : 4.0/5 (85 download)
Download or read book Probabilistic Reasoning in Intelligent Systems written by Judea Pearl and published by Elsevier. This book was released on 2014-06-28 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
Author : Mike Oaksford
Publisher : Oxford University Press
ISBN 13 : 0198524498
Total Pages : 342 pages
Book Rating : 4.1/5 (985 download)
Download or read book Bayesian Rationality written by Mike Oaksford and published by Oxford University Press. This book was released on 2007-02-22 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: For almost 2,500 years, the Western concept of what is to be human has been dominated by the idea that the mind is the seat of reason - humans are, almost by definition, the rational animal. In this text a more radical suggestion for explaining these puzzling aspects of human reasoning is put forward.
Author : Thomas Parr
Publisher : MIT Press
ISBN 13 : 0262362287
Total Pages : 313 pages
Book Rating : 4.2/5 (623 download)
Download or read book Active Inference written by Thomas Parr and published by MIT Press. This book was released on 2022-03-29 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning.
Author : Koen Lamberts
Publisher : Psychology Press
ISBN 13 : 1135064415
Total Pages : 479 pages
Book Rating : 4.1/5 (35 download)
Download or read book Knowledge Concepts and Categories written by Koen Lamberts and published by Psychology Press. This book was released on 2013-10-28 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge, Concepts and Categories brings together an overview of recent research on concepts and knowledge that abstracts across a variety of specific fields of cognitive psychology. Readers will find data from many different areas: developmental psychology, formal modelling, neuropsychology, connectionism, philosophy, and so on. The book can be divided into three parts. Chapters 1 to 5 each contain a thorough and systematic review of a significant aspect of research on concepts and categories. Chapters 6 to 9 are concerned primarily with issues related to the taxonomy of human knowledge. Finally, Chapters 10 to 12 discuss formal models of categorization and function learning. The purpose of these three chapters is to provide a few examples of current formal modelling of conceptual behaviour. Knowledge, Concepts and Categories will be welcomed by students and researchers in cognitive psychology and related areas as an unusually wide-ranging and authoritative review of an important subfield of psychology.
