This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.  It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.Topics and features:Presents a unified framework encompassing all of the main classes of PGMsExplores the fundamental aspects of representation, inference and learning for each techniqueExamines new material on partially observable Markov decision processes, and graphical modelsIncludes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal modelsProvides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projectsDescribes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian NetworksOutlines the practical application of the different techniquesSuggests possible course outlines for instructorsThis classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.  It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.

The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.

Topics and features:

Presents a unified framework encompassing all of the main classes of PGMsExplores the fundamental aspects of representation, inference and learning for each techniqueExamines new material on partially observable Markov decision processes, and graphical modelsIncludes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal modelsProvides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projectsDescribes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian NetworksOutlines the practical application of the different techniquesSuggests possible course outlines for instructors

This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.

Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.  It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.Topics and features:Presents a unified framework encompassing all of the main classes of PGMsExplores the fundamental aspects of representation, inference and learning for each techniqueExamines new material on partially observable Markov decision processes, and graphical modelsIncludes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal modelsProvides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projectsDescribes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian NetworksOutlines the practical application of the different techniquesSuggests possible course outlines for instructorsThis classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

This book presents an overview of causal discovery, an emergent field with important developments in the last few years, and multiple applications in several fields.The book is divided into three parts. The first part provides the necessary background on causal graphical models and causal reasoning. The second describes the main algorithms and techniques for causal discovery: (a) causal discovery from observational data, (b) causal discovery from interventional data, (c) causal discovery from temporal data, and (d) causal reinforcement learning. The third part provides several examples of causal discovery in practice, including applications in biomedicine, social sciences, artificial intelligence and robotics.Topics and features:Includes the necessary background material: a review of probability and graph theory, Bayesian networks, causal graphical models and causal reasoningCovers the main types of causal discovery: learning from observational data, learning from interventional data, and learning from temporal dataIllustrates the application of causal discovery in practical problemsIncludes some of the latest developments in the field, such as continuous optimization, causal event networks, causal discovery under subsampling, subject specific causal models, and causal reinforcement learningProvides chapter exercises, including suggestions for research and programming projectsThis book can be used as a textbook for an advanced undergraduate or a graduate course on causal discovery for students of computer science, engineering, social sciences, etc. It can also be used as a complement to a course on causality, together with another text on causal reasoning. It could also serve as a reference book for professionals that want to apply causal models in different areas, or anyone who is interested in knowing the basis of these techniques. The intended audience are students and professionals in computer science, statistics andengineering who want to know the principles of causal discovery and / or applied them in differentdomains. It could also be of interest to students and professionals in other areas who want to applycausal discovery, for instance in medicine and economics.

This book presents an overview of causal discovery, an emergent field with important developments in the last few years, and multiple applications in several fields.The book is divided into three parts. The first part provides the necessary background on causal graphical models and causal reasoning. The second describes the main algorithms and techniques for causal discovery: (a) causal discovery from observational data, (b) causal discovery from interventional data, (c) causal discovery from temporal data, and (d) causal reinforcement learning. The third part provides several examples of causal discovery in practice, including applications in biomedicine, social sciences, artificial intelligence and robotics.Topics and features:Includes the necessary background material: a review of probability and graph theory, Bayesian networks, causal graphical models and causal reasoningCovers the main types of causal discovery: learning from observational data, learning from interventional data, and learning from temporal dataIllustrates the application of causal discovery in practical problemsIncludes some of the latest developments in the field, such as continuous optimization, causal event networks, causal discovery under subsampling, subject specific causal models, and causal reinforcement learningProvides chapter exercises, including suggestions for research and programming projectsThis book can be used as a textbook for an advanced undergraduate or a graduate course on causal discovery for students of computer science, engineering, social sciences, etc. It can also be used as a complement to a course on causality, together with another text on causal reasoning. It could also serve as a reference book for professionals that want to apply causal models in different areas, or anyone who is interested in knowing the basis of these techniques. The intended audience are students and professionals in computer science, statistics andengineering who want to know the principles of causal discovery and / or applied them in differentdomains. It could also be of interest to students and professionals in other areas who want to applycausal discovery, for instance in medicine and economics.

The Mexican International Conference on Arti?cial Intelligence (MICAI) is a biennial conference established to promote research inarti?cial intelligence (AI), and cooperation among Mexican researchersand their peers worldwide. MICAI is organized by the Mexican Societyfor Arti?cial Intelligence (SMIA), in colla- ration with the AmericanAssociation for Arti?cial Intelligence (AAAI) and the Mexican Society for Computer Science (SMCC). After two successful conferences, we are pleased to present the 3rd Mexican InternationalConferenceonArti?cialIntelligence,MICAI2004,whichtookplace on April 26-30, 2004, in Mexico City, Mexico. This volume contains the papers included in the conferencemain program, which was complemented by tutorials and workshops, published in supplementary proceedings. The proceedings of pastMICAIconferences,2000and2002,werealsopublishedinSpringer-Verlag's Lecture Notes in Arti?cial Intelligence (LNAI) series, volumes 1793 and 2313. The number of submissions to MICAI 2004 was signi?cantly higher than those of previous conferences - 254 papers from 19 di?erent countries were submitted for consideration to MICAI 2004.The evaluation of this unexpectedly largenumberofpaperswasachallenge,bothintermsofthequalityofthepapers and of the review workload of each PC member. After a thorough reviewing process, MICAI's Program Committee and Programs Chairs accepted 97 hi- quality papers. So the acceptance rate was 38.2%. CyberChair, a free Web-based paper submission and reviewing system, was used as an electronic support for the reviewing process. This book contains revised versions of the 94 papers presented at the con- rence. The volume is structured into 13 thematic ?elds according to the topics addressed by the papers, which are representative of the main current area of interest within the AI community.

This book includes the thoroughly refereed post-conference proceedings of the 16th Annual RoboCup International Symposium, held in Mexico City, Mexico, in June 2012. The 24 revised papers presented together with nine champion team papers and one best paper award were carefully reviewed and selected from 64 submissions. The papers present current research and educational activities within the fields of Robotics and Artificial Intelligence with a special focus to robot hardware and software, perception and action, robotic cognition and learning, multi-robot systems, human-robot interaction, education and edutainment, and applications.

This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.  It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.Topics and features:Presents a unified framework encompassing all of the main classes of PGMsExplores the fundamental aspects of representation, inference and learning for each techniqueExamines new material on partially observable Markov decision processes, and graphical modelsIncludes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal modelsProvides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projectsDescribes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian NetworksOutlines the practical application of the different techniquesSuggests possible course outlines for instructorsThis classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.  It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.

The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.

Topics and features:

Presents a unified framework encompassing all of the main classes of PGMsExplores the fundamental aspects of representation, inference and learning for each techniqueExamines new material on partially observable Markov decision processes, and graphical modelsIncludes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal modelsProvides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projectsDescribes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian NetworksOutlines the practical application of the different techniquesSuggests possible course outlines for instructors

This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.

Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.  It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.Topics and features:Presents a unified framework encompassing all of the main classes of PGMsExplores the fundamental aspects of representation, inference and learning for each techniqueExamines new material on partially observable Markov decision processes, and graphical modelsIncludes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal modelsProvides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projectsDescribes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian NetworksOutlines the practical application of the different techniquesSuggests possible course outlines for instructorsThis classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

This book presents an overview of causal discovery, an emergent field with important developments in the last few years, and multiple applications in several fields.The book is divided into three parts. The first part provides the necessary background on causal graphical models and causal reasoning. The second describes the main algorithms and techniques for causal discovery: (a) causal discovery from observational data, (b) causal discovery from interventional data, (c) causal discovery from temporal data, and (d) causal reinforcement learning. The third part provides several examples of causal discovery in practice, including applications in biomedicine, social sciences, artificial intelligence and robotics.Topics and features:Includes the necessary background material: a review of probability and graph theory, Bayesian networks, causal graphical models and causal reasoningCovers the main types of causal discovery: learning from observational data, learning from interventional data, and learning from temporal dataIllustrates the application of causal discovery in practical problemsIncludes some of the latest developments in the field, such as continuous optimization, causal event networks, causal discovery under subsampling, subject specific causal models, and causal reinforcement learningProvides chapter exercises, including suggestions for research and programming projectsThis book can be used as a textbook for an advanced undergraduate or a graduate course on causal discovery for students of computer science, engineering, social sciences, etc. It can also be used as a complement to a course on causality, together with another text on causal reasoning. It could also serve as a reference book for professionals that want to apply causal models in different areas, or anyone who is interested in knowing the basis of these techniques. The intended audience are students and professionals in computer science, statistics andengineering who want to know the principles of causal discovery and / or applied them in differentdomains. It could also be of interest to students and professionals in other areas who want to applycausal discovery, for instance in medicine and economics.

This book presents an overview of causal discovery, an emergent field with important developments in the last few years, and multiple applications in several fields.The book is divided into three parts. The first part provides the necessary background on causal graphical models and causal reasoning. The second describes the main algorithms and techniques for causal discovery: (a) causal discovery from observational data, (b) causal discovery from interventional data, (c) causal discovery from temporal data, and (d) causal reinforcement learning. The third part provides several examples of causal discovery in practice, including applications in biomedicine, social sciences, artificial intelligence and robotics.Topics and features:Includes the necessary background material: a review of probability and graph theory, Bayesian networks, causal graphical models and causal reasoningCovers the main types of causal discovery: learning from observational data, learning from interventional data, and learning from temporal dataIllustrates the application of causal discovery in practical problemsIncludes some of the latest developments in the field, such as continuous optimization, causal event networks, causal discovery under subsampling, subject specific causal models, and causal reinforcement learningProvides chapter exercises, including suggestions for research and programming projectsThis book can be used as a textbook for an advanced undergraduate or a graduate course on causal discovery for students of computer science, engineering, social sciences, etc. It can also be used as a complement to a course on causality, together with another text on causal reasoning. It could also serve as a reference book for professionals that want to apply causal models in different areas, or anyone who is interested in knowing the basis of these techniques. The intended audience are students and professionals in computer science, statistics andengineering who want to know the principles of causal discovery and / or applied them in differentdomains. It could also be of interest to students and professionals in other areas who want to applycausal discovery, for instance in medicine and economics.

The Mexican International Conference on Arti?cial Intelligence (MICAI) is a biennial conference established to promote research inarti?cial intelligence (AI), and cooperation among Mexican researchersand their peers worldwide. MICAI is organized by the Mexican Societyfor Arti?cial Intelligence (SMIA), in colla- ration with the AmericanAssociation for Arti?cial Intelligence (AAAI) and the Mexican Society for Computer Science (SMCC). After two successful conferences, we are pleased to present the 3rd Mexican InternationalConferenceonArti?cialIntelligence,MICAI2004,whichtookplace on April 26-30, 2004, in Mexico City, Mexico. This volume contains the papers included in the conferencemain program, which was complemented by tutorials and workshops, published in supplementary proceedings. The proceedings of pastMICAIconferences,2000and2002,werealsopublishedinSpringer-Verlag's Lecture Notes in Arti?cial Intelligence (LNAI) series, volumes 1793 and 2313. The number of submissions to MICAI 2004 was signi?cantly higher than those of previous conferences - 254 papers from 19 di?erent countries were submitted for consideration to MICAI 2004.The evaluation of this unexpectedly largenumberofpaperswasachallenge,bothintermsofthequalityofthepapers and of the review workload of each PC member. After a thorough reviewing process, MICAI's Program Committee and Programs Chairs accepted 97 hi- quality papers. So the acceptance rate was 38.2%. CyberChair, a free Web-based paper submission and reviewing system, was used as an electronic support for the reviewing process. This book contains revised versions of the 94 papers presented at the con- rence. The volume is structured into 13 thematic ?elds according to the topics addressed by the papers, which are representative of the main current area of interest within the AI community.

This book includes the thoroughly refereed post-conference proceedings of the 16th Annual RoboCup International Symposium, held in Mexico City, Mexico, in June 2012. The 24 revised papers presented together with nine champion team papers and one best paper award were carefully reviewed and selected from 64 submissions. The papers present current research and educational activities within the fields of Robotics and Artificial Intelligence with a special focus to robot hardware and software, perception and action, robotic cognition and learning, multi-robot systems, human-robot interaction, education and edutainment, and applications.