Tag: Stochastic

Free Download Stochastic Choice Theory
English | 2025 | ISBN: 1009512765 | 221 Pages | PDF | 3 MB
Models of stochastic choice are studied in decision theory, discrete choice econometrics, behavioral economics and psychology. Numerous experiments show that perception of stimuli is not deterministic, but stochastic (randomly determined). A growing body of evidence indicates that the same is true of economic choices. Whether trials are separated by days or minutes, the fraction of choice reversals is substantial. Stochastic Choice Theory offers a systematic introduction to these models, unifying insights from various fields. It explores mathematical models of stochastic choice, which have a variety of applications in game theory, industrial organization, labor economics, marketing, and experimental economics. Offering a systematic introduction to the field, this book builds up from scratch without any prior knowledge requirements and surveys recent developments, bringing readers to the frontier of research.

Free Download Applied Financial Stochastic Calculus: A Practical Approach with Real-World Scenarios: A Comprehensive Guide 2025 by Hayden Van Der Post, Reactive Publishing, Alice Schwartz
English | January 8, 2025 | ISBN: N/A | ASIN: B0DSM2BD5K | 352 pages | EPUB | 1.32 Mb
Reactive Publishing

Free Download Dynamical System and Stochastic Analysis by Jun Huang, Yueyuan Zhang
English | PDF (True) | 2024 | 178 Pages | ISBN : 3725825181 | 9.3 MB
Almost all real-world systems are inevitably subject to random structures, parameters, and noises, and stochastic systems have been playing increasingly important roles in all areas of science and engineering.The purpose of this Special Issue is to solicit the recent achievements of control theory and applications of stochastic systems so as to further improve and develop the theoretical methods of stochastic system estimation, fault diagnosis, prognostics, and optimization, among others.

Free Download Variable Gain Design in Stochastic Iterative Learning Control
English | 2024 | ISBN: 9819782805 | 590 Pages | PDF EPUB (True) | 68 MB
This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. The key concept for the gain design is to balance multiple performance indices such as high tracking precision, effective noise reduction, and fast convergence speed. These gain design techniques can be applied to various control algorithms for stochastic systems to realize a high tracking performance. This book provides a series of design and analysis techniques for the establishment of a systematic framework of gain design in SILC. The book is intended for scholars and graduate students who are interested in stochastic control, recursive algorithms design, and iterative learning control.

Free Download Structured Controllers for Uncertain Systems: A Stochastic Optimization Approach By Rosario Toscano (auth.)
2013 | 298 Pages | ISBN: 1447151879 | PDF | 4 MB
Structured Controllers for Uncertain Systems focuses on the development of easy-to-use design strategies for robust low-order or fixed-structure controllers (particularly the industrially ubiquitous PID controller). These strategies are based on a recently-developed stochastic optimization method termed the "Heuristic Kalman Algorithm" (HKA) the use of which results in a simplified methodology that enables the solution of the structured control problem without a profusion of user-defined parameters. An overview of the main stochastic methods employable in the context of continuous non-convex optimization problems is also provided and various optimization criteria for the design of a structured controller are considered; H∞, H2, and mixed H2/H∞ each merits a chapter to itself. Time-domain-performance specifications can be easily incorporated in the design.

Free Download Optimal Control and Optimization of Stochastic Supply Chain Systems By Dong-Ping Song (auth.)
2013 | 272 Pages | ISBN: 1447147235 | PDF | 3 MB
Optimal Control and Optimization of Stochastic Supply Chain Systems examines its subject the context of the presence of a variety of uncertainties. Numerous examples with intuitive illustrations and tables are provided, to demonstrate the structural characteristics of the optimal control policies in various stochastic supply chains and to show how to make use of these characteristics to construct easy-to-operate sub-optimal policies. In Part I, a general introduction to stochastic supply chain systems is provided. Analytical models for various stochastic supply chain systems are formulated and analysed in Part II. In Part III the structural knowledge of the optimal control policies obtained in Part II is utilized to construct easy-to-operate sub-optimal control policies for various stochastic supply chain systems accordingly. Finally, Part IV discusses the optimisation of threshold-type control policies and their robustness. A key feature of the book is its tying together of the complex analytical models produced by the requirements of operational practice, and the simple solutions needed for implementation. The analytical models and theoretical analysis propounded in this monograph will be of benefit to academic researchers and graduate students looking at logistics and supply chain management from standpoints in operations research or industrial, manufacturing, or control engineering. The practical tools and solutions and the qualitative insights into the ideas underlying functional supply chain systems will be of similar use to readers from more industrially-based backgrounds.

Free Download Classical and Spatial Stochastic Processes: With Applications to Biology
English | 2024 | ISBN: 303177759X | 298 Pages | PDF EPUB (True) | 15 MB
This textbook provides an accessible approach to concepts and applications of stochastic processes ideal for a wide range of readers. This revised third edition features an intuitive reorganization with concrete topics introduced early on which are then used to demonstrate more abstract concepts in later chapters. The author has kept chapters short and independent from each other, with several of the longer chapters from previous editions now divided into smaller, more manageable parts. These changes build upon previous editions to allow readers even greater flexibility.

Free Download Convex Stochastic Optimization: Dynamic Programming and Duality in Discrete Time
English | 2024 | ISBN: 3031764315 | 423 Pages | PDF EPUB (True) | 36 MB
This book studies a general class of convex stochastic optimization (CSO) problems that unifies many common problem formulations from operations research, financial mathematics and stochastic optimal control. We extend the theory of dynamic programming and convex duality to allow for a unified and simplified treatment of various special problem classes found in the literature. The extensions allow also for significant generalizations to existing problem formulations. Both dynamic programming and duality have played crucial roles in the development of various optimality conditions and numerical techniques for the solution of convex stochastic optimization problems.

Free Download Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics: International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007. Proceedings By Arne Løkketangen (auth.), Thomas Stützle, Mauro Birattari, Holger H. Hoos (eds.)
2007 | 230 Pages | ISBN: 3540744452 | PDF | 5 MB
Stochastic local search (SLS) algorithms enjoy great popularity as powerful and versatile tools for tackling computationally hard decision and optimization pr- lems from many areas of computer science, operations research, and engineering. To a large degree, this popularity is based on the conceptual simplicity of many SLS methods and on their excellent performance on a wide gamut of problems, ranging from rather abstract problems of high academic interest to the very s- ci?c problems encountered in many real-world applications. SLS methods range from quite simple construction procedures and iterative improvement algorithms to more complex general-purpose schemes, also widely known as metaheuristics, such as ant colony optimization, evolutionary computation, iterated local search, memetic algorithms, simulated annealing, tabu search and variable neighborhood search. Historically, the development of e?ective SLS algorithms has been guided to a large extent by experience and intuition, and overall resembled more an art than a science. However, in recent years it has become evident that at the core of this development task there is a highly complex engineering process, which combines various aspects of algorithm design with empirical analysis techniques and problem-speci?c background, and which relies heavily on knowledge from a number of disciplines and areas, including computer science, operations research, arti?cial intelligence, and statistics. This development process needs to be – sisted by a sound methodology that addresses the issues arising in the various phases of algorithm design, implementation, tuning, and experimental eval- tion.

Free Download Stochastic Finance with Python: Design Financial Models from Probabilistic Perspective
English | 2024 | ASIN: B0DGC1T3YB | 499 Pages | PDF EPUB (True) | 26 MB
Journey through the world of stochastic finance from learning theory, underlying models, and derivations of financial models (stocks, options, portfolios) to the almost production-ready Python components under cover of stochastic finance. This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python.