Download or read book Assistant Power Plant Operator written by National Learning Corporation and published by . This book was released on 1976 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Assistant Power Plant Operator written by National Learning Corporation and published by National Learning Corporation. This book was released on 2020-02 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Assistant Power Plant Operator Passbook(R) prepares you for your test by allowing you to take practice exams in the subjects you need to study.

Download or read book Power Plant Operator written by National Learning Corporation and published by National Learning Corporation. This book was released on 2019-02 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Power Plant Operator Passbook(R) prepares you for your test by allowing you to take practice exams in the subjects you need to study. It provides hundreds of questions and answers in the areas that will likely be covered on your upcoming exam, including but not limited to: reading comprehension and basic mathematics; tools, mechanical aptitude, and the reading of scales and gauges; operation, maintenance and repair of pumps, motors, valves, electrical and mechanical equipment; elementary hydraulics and water pumping operations; operation and maintenance of diesel engines; and more.

Download or read book POWER PLANT OPERATOR written by National Learning Corporation and published by . This book was released on 2019 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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Download or read book Probabilistic Graphical Models written by Luis Enrique Sucar and published by Springer Nature. This book was released on 2020-12-23 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests 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.

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