Stochastic Algorithms

Comparison Methods For Stochastic Models & Risks

90 折

81.

Comparison Methods For Stochastic Models & Risks

出版日：2002/02/06 作者：Muller 出版社：John Wiley & Sons Inc 裝訂：精裝

Stochastic order relations prprovide a valuable insight into the behaviour of complex stochastic (random) systems and enable the user to collect meaningful comparative data. Application areas include

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Stochastic Flows and Stochastic Differential Equations

90 折

82.

Stochastic Flows and Stochastic Differential Equations

出版日：1997/04/03 作者：Hiroshi Kunita 出版社：Cambridge Univ Pr 裝訂：平裝

The main purpose of this book is to give a systematic treatment of the theory of stochastic differential equations and stochastic flow of diffeomorphisms, and through the former to study the properties of stochastic flows. The classical theory was initiated by K. Itô and since then has been much developed. Professor Kunita's approach here is to regard the stochastic differential equation as a dynamical system driven by a random vector field, including thereby Itô's theory as a special case. The book can be used with advanced courses on probability theory or for self-study. The author begins with a discussion of Markov processes, martingales and Brownian motion, followed by a review of Itô's stochastic analysis. The next chapter deals with continuous semimartingales with spatial parameters, in order to study stochastic flow, and a generalisation of Ito's equation. Stochastic flows and their relation with this are generalised and considered in chapter 4. It is shown that solutions of a g

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83.

A Course in Stochastic Processes ― Stochastic Models and Statistical Inference

出版日：1996/06/01 作者：Denis Bosq; Hung T. Nguyen 出版社：Kluwer Academic Print on Demand 裝訂：精裝

This volume is an introduction to stochastic processes and their statistics. Basic stochastic processes are developed from real world situations to the need for generating mathematical models, while a

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84.

Optimization of Stochastic Models: The Interface Between Simulation and Optimization

出版日：1996/06/01 作者：George Ch Pflug 出版社：Kluwer Academic Pub 裝訂：平裝

Stochastic models are everywhere. In manufacturing, queuing models are used for modeling production processes, realistic inventory models are stochastic in nature. Stochastic models are considered

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90 折

85.

Sequential Stochastic Optimization

出版日：1996/01/19 作者：Cairoli 出版社：John Wiley & Sons Inc 裝訂：精裝

Sequential Stochastic Optimization provides mathematicians and applied researchers with a well-developed framework in which stochastic optimization problems can be formulated and solved. Offering much

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90 折

86.

Randomized Algorithms

出版日：1995/08/25 作者：Rajeev Motwani 出版社：Cambridge Univ Pr 裝訂：精裝

For many applications a randomized algorithm is either the simplest algorithm available, or the fastest, or both. This tutorial presents the basic concepts in the design and analysis of randomized algorithms. The first part of the book presents tools from probability theory and probabilistic analysis that are recurrent in algorithmic applications. Algorithmic examples are given to illustrate the use of each tool in a concrete setting. In the second part of the book, each of the seven chapters focuses on one important area of application of randomized algorithms: data structures; geometric algorithms; graph algorithms; number theory; enumeration; parallel algorithms; and on-line algorithms. A comprehensive and representative selection of the algorithms in these areas is also given. This book should prove invaluable as a reference for researchers and professional programmers, as well as for students.

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Stochastic Partial Differential Equations

90 折

87.

Stochastic Partial Differential Equations

出版日：1995/07/13 作者：Alison Etheridge 出版社：Cambridge Univ Pr 裝訂：平裝

Stochastic partial differential equations can be used in many areas of science to model complex systems that evolve over time. Their analysis is currently an area of much research interest. This book consists of papers given at the ICMS Edinburgh meeting held in 1994 on this topic, and it brings together some of the world's best known authorities on stochastic partial differential equations. Subjects covered include the stochastic Navier–Stokes equation, critical branching systems, population models, statistical dynamics, and ergodic properties of Markov semigroups. For all workers on stochastic partial differential equations this book will have much to offer.

優惠價： 9 3451

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88.

Stochastic Programming

出版日：1995/07/01 作者：Andras Prekopa 出版社：Springer Verlag 裝訂：平裝

Stochastic programming - the science that provides us with tools to design and control stochastic systems with the aid of mathematical programming techniques - lies at the intersection of statistic

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89.

Stochastic Programming

出版日：1995/07/01 作者：A. Prekopa 出版社：Springer Verlag 裝訂：精裝

Stochastic Programming is the science that provides us with tools to design and control stochastic systems with the aid of mathematical programming techniques. It is on the border line of statistics a

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Numerical Methods For Stochastic Processes

90 折

90.

Numerical Methods For Stochastic Processes

出版日：1993/12/17 作者：Bouleau 出版社：John Wiley & Sons Inc 裝訂：精裝

Gives greater rigor to numerical treatments of stochastic models. Contains Monte Carlo and quasi-Monte Carlo techniques, simulation of major stochastic procedures, deterministic methods adapted to Mar

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Semimartingale Theory and Stochastic Calculus

90 折

91.

Semimartingale Theory and Stochastic Calculus

出版日：1992/09/01 作者：Sheng-Wu He; Jia-Gang Wang; Jia-An Yan 出版社：CRC Press UK 裝訂：平裝

Semimartingale Theory and Stochastic Calculus presents a systematic and detailed account of the general theory of stochastic processes, the semimartingale theory, and related stochastic calculus. The

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92.

Adventures in Stochastic Processes

出版日：1992/07/01 作者：Sidney Resnick 出版社：Springer Verlag 裝訂：精裝

This textbook for first year graduate courses (usually called Stochastic Processes, Applied Probability, or Stochastic Modeling) is subtitled, The random world of Happy Harry, and is filled with exa

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Introduction to Algorithms, fourth edition

79 折

93.

Introduction to Algorithms, fourth edition

出版日：2022/03/22 作者：Thomas H. Cormen 出版社：Mit Pr 裝訂：精裝

A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learningNew material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays140 new exerc

優惠價： 79 7110

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滿額折

94.

Optimization for Data Analysis

出版日：2021/10/31 作者：Stephen J. Wright 出版社：Cambridge Univ Pr 裝訂：精裝

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; found

優惠價： 9 2222

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90 折

95.

Algorithms for Convex Optimization

出版日：2021/07/31 作者：Nisheeth K. Vishnoi 出版社：Cambridge Univ Pr 裝訂：精裝

In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.

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90 折

96.

Algorithms for Convex Optimization

出版日：2021/07/31 作者：Nisheeth K. Vishnoi 出版社：Cambridge Univ Pr 裝訂：平裝

In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.

優惠價： 9 1781

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The Cambridge Handbook of the Law of Algorithms

90 折

97.

The Cambridge Handbook of the Law of Algorithms

出版日：2020/10/31 作者：Woodrow Barfield 出版社：Cambridge Univ Pr 裝訂：精裝

Algorithms are a fundamental building block of artificial intelligence - and, increasingly, society - but our legal institutions have largely failed to recognize or respond to this reality. The Cambridge Handbook of the Law of Algorithms, which features contributions from US, EU, and Asian legal scholars, discusses the specific challenges algorithms pose not only to current law, but also - as algorithms replace people as decision makers - to the foundations of society itself. The work includes wide coverage of the law as it relates to algorithms, with chapters analyzing how human biases have crept into algorithmic decision-making about who receives housing or credit, the length of sentences for defendants convicted of crimes, and many other decisions that impact constitutionally protected groups. Other issues covered in the work include the impact of algorithms on the law of free speech, intellectual property, and commercial and human rights law.

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Algorithms for the Adventurous ― Creative Solutions to Computational (And Human) Problems

滿額折

98.

Algorithms for the Adventurous ― Creative Solutions to Computational (And Human) Problems

出版日：2020/10/13 作者：Bradford Tuckfield 出版社：No Starch Pr 裝訂：平裝

Dive Into Algorithms is a broad introduction to algorithms using the Python Programming Language.Dive Into Algorithms is a wide-ranging, Pythonic tour of many of the world's most interesting algorithm

優惠價： 79 1199

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99.

Algorithms

出版日：2020/08/18 作者：Panos Louridas 出版社：Mit Pr 裝訂：平裝

An accessible introduction to algorithms, explaining not just what they are but how they work, with examples from a wide range of application areas.Digital technology runs on algorithms, sets of instr

優惠價： 79 509

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Stochastic Stability of Differential Equations in Abstract Spaces

90 折

100.

Stochastic Stability of Differential Equations in Abstract Spaces

出版日：2019/06/30 作者：Kai Liu 出版社：Cambridge Univ Pr 裝訂：平裝

The stability of stochastic differential equations in abstract, mainly Hilbert, spaces receives a unified treatment in this self-contained book. It covers basic theory as well as computational techniques for handling the stochastic stability of systems from mathematical, physical and biological problems. Its core material is divided into three parts devoted respectively to the stochastic stability of linear systems, non-linear systems, and time-delay systems. The focus is on stability of stochastic dynamical processes affected by white noise, which are described by partial differential equations such as the Navier–Stokes equations. A range of mathematicians and scientists, including those involved in numerical computation, will find this book useful. It is also ideal for engineers working on stochastic systems and their control, and researchers in mathematical physics or biology.

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101.

Stochastic Control and Filtering over Constrained Communication Networks

出版日：2018/11/03 作者：Qinyuan Liu; Zidong Wang; Xiao He 出版社：Springer Nature 裝訂：精裝

?Stochastic Control and Filtering over Constrained Communication Networks presents up-to-date research developments and novel methodologies on stochastic control and filtering for networked syste

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Stochastic Evolution Systems ― Linear Theory and Applications to Non-linear Filtering

90 折

102.

Stochastic Evolution Systems ― Linear Theory and Applications to Non-linear Filtering

出版日：2018/10/15 作者：Boris L. Rozovsky; Sergey V. Lototsky 出版社：Springer Nature 裝訂：精裝

This monograph, now in a thoroughly revised second edition, develops the theory of stochastic calculus in Hilbert spaces and applies the results to the study of generalized solutions of stochastic par

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103.

Introduction to Stochastic Calculus

出版日：2018/06/29 作者：Rajeeva L. Karandikar; B. V. Rao 出版社：Springer Verlag 裝訂：精裝

This book sheds new light on stochastic calculus, the branch of mathematics that is most widely applied in financial engineering and mathematical finance. The first book to introduce pathwise formulae

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104.

Bioinformatic Algorithms ─ Design and Implementation in Python

出版日：2018/02/01 作者：Miguel Rocha; Pedro G. Ferreira 出版社：Academic Pr 裝訂：平裝

Bioinformatics Algorithms: Design and Implementation in Python provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how

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105.

Bio-inspired Algorithms for Engineering

出版日：2018/02/01 作者：Nancy Arana-daniel; Carlos Lopez-franco; Alma Y. Alanis 出版社：Butterworth-Heinemann 裝訂：平裝

Bio-inspired Algorithms for Engineering builds a bridge between the proposed bio-inspired algorithms developed in the past few decades and their applications in real-life problems, not only in an acad

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Quantum Monte Carlo Approaches for Correlated Systems

滿額折

106.

Quantum Monte Carlo Approaches for Correlated Systems

出版日：2018/01/31 作者：Federico Becca 出版社：Cambridge Univ Pr 裝訂：精裝

Over the past several decades, computational approaches to studying strongly-interacting systems have become increasingly varied and sophisticated. This book provides a comprehensive introduction to state-of-the-art quantum Monte Carlo techniques relevant for applications in correlated systems. Providing a clear overview of variational wave functions, and featuring a detailed presentation of stochastic samplings including Markov chains and Langevin dynamics, which are developed into a discussion of Monte Carlo methods. The variational technique is described, from foundations to a detailed description of its algorithms. Further topics discussed include optimisation techniques, real-time dynamics and projection methods, including Green's function, reptation and auxiliary-field Monte Carlo, from basic definitions to advanced algorithms for efficient codes, and the book concludes with recent developments on the continuum space. Quantum Monte Carlo Approaches for Correlated Systems provides

優惠價： 9 3275

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90 折

107.

Theory of Stochastic Objects

出版日：2018/01/29 作者：Athanasios Christou Micheas 出版社：Chapman & Hall 裝訂：精裝

This book introduces the theory of point processes and random objects. It begins by providing necessary background on inference, probability, measure theory, and stochastic processes. It then covers p

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滿額折

108.

Stochastic Drawdowns

出版日：2017/09/30 作者：Hongzhong Zhang 出版社：World Scientific Pub Co Inc 裝訂：精裝

Stochastic Drawdowns consists of some recent advances on Hongzhong Zhang's own quantitative research of the well-known risk measures, drawdowns and maximum drawdowns. In particular, the author provide

優惠價： 9 2999

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109.

Backward Stochastic Differential Equations ― From Linear to Fully Nonlinear Theory

出版日：2017/08/22 作者：Jianfeng Zhang 出版社：Springer Verlag 裝訂：精裝

This book provides a systematic and accessible approach to stochastic differential equations, backward stochastic differential equations, and their connection with partial differential equations, as w

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Selected Papers on Noise and Stochastic Processes

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110.

Selected Papers on Noise and Stochastic Processes

出版日：2017/06/14 作者：Nelson Wax (EDT) 出版社：Dover Pubns 裝訂：平裝

These six classic papers on stochastic process were selected to meet the needs of physicists, applied mathematicians, and engineers. Contents include S. Chandrasekhar's "Stochastic Problems in Physics

優惠價： 9 648

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Stochastic Dynamics, Filtering and Optimization

90 折

111.

Stochastic Dynamics, Filtering and Optimization

出版日：2017/04/30 作者：Debasish Roy 出版社：Cambridge Univ Pr 裝訂：精裝

Targeted at graduate students, researchers and practitioners in the field of science and engineering, this book gives a self-contained introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes. It then continues to weave into a framework of several practical tools and applications involving stochastic dynamical systems. These include tools for the numerical integration of such dynamical systems, nonlinear stochastic filtering and generalized Bayesian update theories for solving inverse problems and a new stochastic search technique for treating a broad class of non-convex optimization problems. MATLAB® codes for all the applications are uploaded on the companion website.

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90 折

112.

Evolutionary Algorithms

出版日：2017/04/14 作者：Petrowski 出版社：John Wiley & Sons Inc 裝訂：精裝

Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is imp

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Stochastic Differential Equations: An Introduction With Applications In Population Dynamics Modeling

90 折

113.

Stochastic Differential Equations: An Introduction With Applications In Population Dynamics Modeling

出版日：2017/03/24 作者：Panik 出版社：John Wiley & Sons Inc 裝訂：精裝

This book uses stochastic differential equation (SDE) models to determine a variety of population growth equations. SDEs represent mathematical models that combine deterministic and stochastic compone

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Real-World Algorithms ─ A Beginner's Guide

79 折

114.

Real-World Algorithms ─ A Beginner's Guide

出版日：2017/03/17 作者：Panos Louridas 出版社：Mit Pr 裝訂：精裝

Algorithms are what we do in order not to have to do something. Algorithms consist of instructions to carry out tasks -- usually dull, repetitive ones. Starting from simple building blocks, computer a

優惠價： 79 1351

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115.

Algorithms for Dummies

出版日：2017/02/21 作者：John Paul Mueller; Luca Massaron 出版社：For Dummies 裝訂：平裝

Algorithms For Dummies addresses people who are interested in algorithms without requiring them to pursue a PhD on the subject. The idea is that we already live in a world where algorithms are behind

優惠價： 9 1026

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90 折

116.

Algorithms for Data Science

出版日：2017/01/23 作者：Brian Steele; John Chandler; Swarna Reddy 出版社：Springer-Verlag New York Inc 裝訂：精裝

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive e

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90 折

117.

Essentials of Stochastic Processes

出版日：2016/11/17 作者：Richard Durrett 出版社：Springer Verlag 裝訂：精裝

Building upon the previous editions, this textbook is a first course in stochastic processes taken by undergraduate and graduate students (MS and PhD students from math, statistics, economics, compute

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Grouping Genetic Algorithms ― Advances and Applications

90 折

118.

Grouping Genetic Algorithms ― Advances and Applications

出版日：2016/10/12 作者：Michael Mutingi; Charles Mbohwa 出版社：Springer-Verlag New York Inc 裝訂：精裝

This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industri

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119.

Nature-inspired Optimization Algorithms

出版日：2016/07/31 作者：Xin-she Yang 出版社：Elsevier Science Ltd 裝訂：平裝

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theo

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Partially Observed Markov Decision Processes ─ From Filtering to Controlled Sensing

90 折

120.

Partially Observed Markov Decision Processes ─ From Filtering to Controlled Sensing

出版日：2016/05/12 作者：Vikram Krishnamurthy 出版社：Cambridge Univ Pr 裝訂：精裝

Covering formulation, algorithms, and structural results, and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. Bringing together research from across the literature, the book provides an introduction to nonlinear filtering followed by a systematic development of stochastic dynamic programming, lattice programming and reinforcement learning for POMDPs. Questions addressed in the book include: when does a POMDP have a threshold optimal policy? When are myopic policies optimal? How do local and global decision makers interact in adaptive

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熱搜：

Comparison Methods For Stochastic Models & Risks

Stochastic Flows and Stochastic Differential Equations

A Course in Stochastic Processes ― Stochastic Models and Statistical Inference

Optimization of Stochastic Models: The Interface Between Simulation and Optimization

Sequential Stochastic Optimization

Randomized Algorithms

Stochastic Partial Differential Equations

Stochastic Programming

Stochastic Programming

Numerical Methods For Stochastic Processes

Semimartingale Theory and Stochastic Calculus

Adventures in Stochastic Processes

Introduction to Algorithms, fourth edition

Optimization for Data Analysis

Algorithms for Convex Optimization

Algorithms for Convex Optimization

The Cambridge Handbook of the Law of Algorithms

Algorithms for the Adventurous ― Creative Solutions to Computational (And Human) Problems

Algorithms

Stochastic Stability of Differential Equations in Abstract Spaces

Stochastic Control and Filtering over Constrained Communication Networks

Stochastic Evolution Systems ― Linear Theory and Applications to Non-linear Filtering

Introduction to Stochastic Calculus

Bioinformatic Algorithms ─ Design and Implementation in Python

Bio-inspired Algorithms for Engineering

Quantum Monte Carlo Approaches for Correlated Systems

Theory of Stochastic Objects

Stochastic Drawdowns

Backward Stochastic Differential Equations ― From Linear to Fully Nonlinear Theory

Selected Papers on Noise and Stochastic Processes

Stochastic Dynamics, Filtering and Optimization

Evolutionary Algorithms

Stochastic Differential Equations: An Introduction With Applications In Population Dynamics Modeling

Real-World Algorithms ─ A Beginner's Guide

Algorithms for Dummies

Algorithms for Data Science

Essentials of Stochastic Processes

Grouping Genetic Algorithms ― Advances and Applications

Nature-inspired Optimization Algorithms

Partially Observed Markov Decision Processes ─ From Filtering to Controlled Sensing