Simulation based policy iteration for American style derivatives - a multilevel approach PDF Download
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Author: Marcel Ladkau
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
Literaturverz. S. 13 - 14
Author: Marcel Ladkau
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
Literaturverz. S. 13 - 14
Author: Denis Belomestny
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
Author: Denis Belomestny
Publisher: Springer
ISBN: 1137033517
Category : Business & Economics
Languages : en
Pages : 366
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Book Description
This is an advanced guide to optimal stopping and control, focusing on advanced Monte Carlo simulation and its application to finance. Written for quantitative finance practitioners and researchers in academia, the book looks at the classical simulation based algorithms before introducing some of the new, cutting edge approaches under development.
Author: Denis Belomestny
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
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Book Description
Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549
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Book Description
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Author: Kenneth Train
Publisher: Cambridge University Press
ISBN: 0521766559
Category : Business & Economics
Languages : en
Pages : 399
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Book Description
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Author: A Villa
Publisher: CRC Press
ISBN: 9780849383328
Category : Mathematics
Languages : en
Pages : 426
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Book Description
This book presents the mathematical models applicable to manufacturing systems management, covering problems from production to real time control. It explores manufacturing systems from the viewpoints of both physical structure and performance measures. Two broad classes of mathematical models are covered in detail: Generative models, which yield a set of decision variables optimizing a performance measure, based on mathematical optimization Evaluative models, which evaluate some performance measures as a function of some predefined decision strategy. Within this class Petri Nets and Queueing Networks are discussed. Advanced Models for Manufacturing Systems Management describes dynamic systems modeling by state equations, a unifying framework for a wide variety of models. The text/reference stresses model building, but it examines model solving as well. Computational techniques are illustrated, such as linear programming, branch and bound methods, and dynamic programming. Particular emphasis is given to the development of heuristic methods from mathematical models. The book provides readers with valuable tools for management and design. The use of descriptive models within an optimization algorithm is considered. Numerous examples illustrate theoretical concepts throughout text. Appendices are given at the end of the book in order to recall fundamentals, such as linear programming and graph theory. Appendices also appear within each chapter. In this way, readers can follow the main reading path without getting involved with details; these appendices can be read at a later time. This textual structure makes this book particularly well suited for self-study. Advanced Models for Manufacturing Systems Management is beneficial reading for both students and practitioners.
Author: Warren B. Powell
Publisher: John Wiley & Sons
ISBN: 1119815037
Category : Mathematics
Languages : en
Pages : 1090
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Book Description
REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
Author: Peter Jäckel
Publisher: John Wiley & Sons
ISBN: 047149741X
Category : Business & Economics
Languages : de
Pages : 245
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Book Description
An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing complexity of derivatives portfolios. Ranging from pricing more complex derivatives, such as American and Asian options, to measuring Value at Risk, or modelling complex market dynamics, simulation is the only method general enough to capture the complexity and Monte Carlo simulation is the best pricing and risk management method available. The book is packed with numerous examples using real world data and is supplied with a CD to aid in the use of the examples.
Author: Yousef Saad
Publisher: SIAM
ISBN: 0898715342
Category : Mathematics
Languages : en
Pages : 537
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Book Description
Mathematics of Computing -- General.
