Stochastic Processes And Their Applications In Artificial Intelligence PDF Download
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| Author | : Ananth, Christo |
| Publisher | : IGI Global |
| Total Pages | : 238 |
| Release | : 2023-07-10 |
| Genre | : Mathematics |
| ISBN | : 1668476819 |
Download Stochastic Processes and Their Applications in Artificial Intelligence Book in PDF, ePub and Kindle
Stochastic processes have a wide range of applications ranging from image processing, neuroscience, bioinformatics, financial management, and statistics. Mathematical, physical, and engineering systems use stochastic processes for modeling and reasoning phenomena. While comparing AI-stochastic systems with other counterpart systems, we are able to understand their significance, thereby applying new techniques to obtain new real-time results and solutions. Stochastic Processes and Their Applications in Artificial Intelligence opens doors for artificial intelligence experts to use stochastic processes as an effective tool in real-world problems in computational biology, speech recognition, natural language processing, and reinforcement learning. Covering key topics such as social media, big data, and artificial intelligence models, this reference work is ideal for mathematicians, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.
| Author | : Jingzheng Ren |
| Publisher | : Elsevier |
| Total Pages | : 542 |
| Release | : 2021-06-05 |
| Genre | : Technology & Engineering |
| ISBN | : 012821743X |
Download Applications of Artificial Intelligence in Process Systems Engineering Book in PDF, ePub and Kindle
Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering
| Author | : M.J. Beckmann |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 996 |
| Release | : 1991-12-11 |
| Genre | : Business & Economics |
| ISBN | : 9783540546351 |
Download Stochastic Processes and their Applications Book in PDF, ePub and Kindle
This volume deals with Stochastic tools with special reference to applications in the areas of Physics, Biology and Operations Research. Quitea few of the papers deal with the applications of the rich theory of point processes in Physics and Operations Research. A few of the papers deal with the problems of Inference and Stochastic theory. In addition papers of some leading specialists are included. These papers reflect the latest trends in these areas and will, therefore, be of value and interest to researchers in these fields.
| Author | : Alexey Piunovskiy |
| Publisher | : Springer Nature |
| Total Pages | : 356 |
| Release | : 2021-06-04 |
| Genre | : Technology & Engineering |
| ISBN | : 3030769283 |
Download Modern Trends in Controlled Stochastic Processes: Book in PDF, ePub and Kindle
This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported. Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented. Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference.
| Author | : Michael M. Richter |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2022-10-01 |
| Genre | : Computers |
| ISBN | : 9783319453712 |
Download Signal Processing and Machine Learning with Applications Book in PDF, ePub and Kindle
Signal processing captures, interprets, describes and manipulates physical phenomena. Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book presents different kinds of signals humans use and applies them for human machine interaction to communicate. Signal Processing and Machine Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications. It is organized in three parts: Realms of Signal Processing; Machine Learning and Recognition; and Advanced Applications and Artificial Intelligence. The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes. The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engaged with signal processing, machine learning and the applications.
| Author | : Alexey B. Piunovskiy |
| Publisher | : Luniver Press |
| Total Pages | : 342 |
| Release | : 2010-09 |
| Genre | : Mathematics |
| ISBN | : 1905986300 |
Download Modern Trends in Controlled Stochastic Processes Book in PDF, ePub and Kindle
World leading experts give their accounts of the modern mathematical models in the field: Markov Decision Processes, controlled diffusions, piece-wise deterministic processes etc, with a wide range of performance functionals. One of the aims is to give a general view on the state-of-the-art. The authors use Dynamic Programming, Convex Analytic Approach, several numerical methods, index-based approach and so on. Most chapters either contain well developed examples, or are entirely devoted to the application of the mathematical control theory to real life problems from such fields as Insurance, Portfolio Optimization and Information Transmission. The book will enable researchers, academics and research students to get a sense of novel results, concepts, models, methods, and applications of controlled stochastic processes.
| Author | : Olivier Sigaud |
| Publisher | : John Wiley & Sons |
| Total Pages | : 367 |
| Release | : 2013-03-04 |
| Genre | : Technology & Engineering |
| ISBN | : 1118620100 |
Download Markov Decision Processes in Artificial Intelligence Book in PDF, ePub and Kindle
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.
| Author | : Howard M. Taylor |
| Publisher | : Academic Press |
| Total Pages | : 410 |
| Release | : 2014-05-10 |
| Genre | : Mathematics |
| ISBN | : 1483269272 |
Download An Introduction to Stochastic Modeling Book in PDF, ePub and Kindle
An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.
| Author | : Jacek Fabian |
| Publisher | : |
| Total Pages | : 316 |
| Release | : 2016-10-01 |
| Genre | : |
| ISBN | : 9781681176482 |
Download Stochastic Processes and Applications Book in PDF, ePub and Kindle
The field of stochastic processes is essentially a branch of probability theory, treating probabilistic models that evolve in time. It is best viewed as a branch of mathematics, starting with the axioms of probability and containing a rich and fascinating set of results following from those axioms. Although the results are applicable to many areas, they are best understood initially in terms of their mathematical structure and interrelationships. Applying axiomatic probability results to a real-world area requires creating a probability model for the given area. Stochastic processes were first studied rigorously in the late 19th century to aid in understanding financial markets and Brownian motion. These subjects originally had an application emphasis, the first on queueing and congestion in data networks and the second on modulation and detection of signals in the presence of noise. It has become increasingly clear that the mathematical development is applicable to a much broader set of applications in engineering, operations research, physics, biology, economics, finance, statistics, etc. Stochastic Processes and their Applications emphasizes on the theory and applications of stochastic processes. It is concerned with concepts and techniques, and is oriented towards a broad spectrum of mathematical, scientific and engineering interests. Characterization, structural properties, inference and control of stochastic processes are covered.
| Author | : Robert P. Dobrow |
| Publisher | : John Wiley & Sons |
| Total Pages | : 503 |
| Release | : 2016-03-07 |
| Genre | : Mathematics |
| ISBN | : 1118740653 |
Download Introduction to Stochastic Processes with R Book in PDF, ePub and Kindle
An introduction to stochastic processes through the use of R Introduction to Stochastic Processes with R is an accessible and well-balanced presentation of the theory of stochastic processes, with an emphasis on real-world applications of probability theory in the natural and social sciences. The use of simulation, by means of the popular statistical software R, makes theoretical results come alive with practical, hands-on demonstrations. Written by a highly-qualified expert in the field, the author presents numerous examples from a wide array of disciplines, which are used to illustrate concepts and highlight computational and theoretical results. Developing readers’ problem-solving skills and mathematical maturity, Introduction to Stochastic Processes with R features: More than 200 examples and 600 end-of-chapter exercises A tutorial for getting started with R, and appendices that contain review material in probability and matrix algebra Discussions of many timely and stimulating topics including Markov chain Monte Carlo, random walk on graphs, card shuffling, Black–Scholes options pricing, applications in biology and genetics, cryptography, martingales, and stochastic calculus Introductions to mathematics as needed in order to suit readers at many mathematical levels A companion web site that includes relevant data files as well as all R code and scripts used throughout the book Introduction to Stochastic Processes with R is an ideal textbook for an introductory course in stochastic processes. The book is aimed at undergraduate and beginning graduate-level students in the science, technology, engineering, and mathematics disciplines. The book is also an excellent reference for applied mathematicians and statisticians who are interested in a review of the topic.
