Fast stochastic ordinal embedding with variance reduction and adaptive step size

Fast stochastic ordinal embedding with variance reduction and adaptive step size
 1.29MB
论文研究Nonlinear stochastic variance reduction gradient based neural networks and its convergence.pdf
20190822基于神经网络的非线性随机梯度学习算法及其收敛性，王健，杨国玲，在实际问题中,大量的最优问题是非凸最优问题。而随机梯度学习算法（SVRG）提供了针对这类问题的一种解决方法,即该算法能够通过训练
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Forwardbackward doubly stochastic differential equations with random jumps and stochastic partial differentialintegral equations
20200310Forwardbackward doubly stochastic differential equations with random jumps and stochastic partial differentialintegral equations，朱庆峰，石玉峰，A type of forwardbackward doubly stochastic differential equations driven by Brownian motions and Poisson process (FBDSDEP in short) is studied. Both the proba
 1.99MB
Stochastic Network Optimization with Application to Communication
20180829Stochastic Network Optimization with Application to Communication and Queueing Systems 外文书籍
 1.38MB
stochastic network optimization with application to communication and queueing
20180604This text is written to teach the theory of Lyapunov drift and Lyapunov optimization for stochastic network optimization. It assumes only that the reader is familiar with basic probability concepts (such as expectations and the law of large numbers). Familiarity with Markov chains and with standard (nonstochastic) optimization is useful but not required. A variety of examples and simulation results are given to illustrate the main concepts. Diverse problem set questions (several with example solutions) are also given. These questions and examples were developed over several years for use in the stochastic network optimization course taught by the author. They include topics of wireless opportunistic scheduling, multihop routing, network coding for maximum throughput, distortionaware data compression, energyconstrained and delayconstrained queueing, dynamic decision making for maximum profit, and more.
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Backward Doubly Stochastic Differential Equations with Jumps and Stochastic Partial DifferentialIntegral Equations
20191229Backward Doubly Stochastic Differential Equations with Jumps and Stochastic Partial DifferentialIntegral Equations，朱庆峰，石玉峰，A type of backward doubly stochastic differential equations driven by Brownian motions and Poisson process (BDSDEP in short) with nonLipschitzian coefficients on random time inter
 10.32MB
Introduction.to.Stochastic.Processes.with.R
20170104An introduction to stochastic processes through the use of R Introduction to Stochastic Processes with R is an accessible and wellbalanced presentation of the theory of stochastic processes, with an emphasis on realworld 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, handson demonstrations. Written by a highlyqualified 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’ problemsolving skills and mathematical maturity, Introduction to Stochastic Processes with R features: More than 200 examples and 600 endofchapter 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 graduatelevel 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. Table of Contents Chapter 1 Introduction and Review Chapter 2 Markov Chains: First Steps Chapter 3 Markov Chains for the Long Term Chapter 4 Branching Processes Chapter 5 Markov Chain Monte Carlo Chapt
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英文原版Introduction to Stochastic Processes with R 1st Edition
20190923An introduction to stochastic processes through the use of RIntroduction to Stochastic Processes with R is an accessible and wellbalanced presentation of the theory of stochastic processes, with an emphasis on realworld 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, handson demonstrations.Written by a highlyqualified 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’ problemsolving skills and mathematical maturity, Introduction to Stochastic Processes with R features:,解压密码 share.weimo.info
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stochastic network optimization with application to communication
20180723stochastic network optimization with application to communication Michael J. N 著 2010版
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Introduction to stochastic calculus with applications.pdf
20190529Introduction to stochastic calculus with applications.pdf Fima C.Klebaner
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Probability and Stochastic Processes(ISBN 0471272140)Problem解答
20150917Probability and Stochastic Processes(ISBN 0471272140)Problem解答
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Stochastic Geometry and Wireless Networks Volume I
20150630This monograph surveys recent results on the use of stochastic geometry for the performance analysis of large wireless networks. It is structured in two volumes. Volume I focuses on stochastic geometry and on the evaluation of spatial averages within this context. It contains two main parts, one on classical stochastic geometry (point processes, Boolean models, percolation, random tessellations, shot noise fields, etc.) and one on a new branch of stochastic geometry which is based on information theoretic notions, such as signal to interference ratios, and which is motivated by the modeling of wireless networks. This second part revisits several basic questions of classical stochastic geometry such as coverage or connectivity within this new framework. Volume II  Applications (see http://hal.inria.fr/inria00403040) bears on more practical wireless network modeling and performance analysis. It leverages the tools developed in Volume I to build the timespace framework needed for analyzing the phenomena which arise in these networks. The first part of Volume II focuses on medium access control protocols used in mobile ad hoc networks and in cellular networks. The second part bears on the analysis of routing algorithms used in mobile ad hoc networks. For readers with a main interest in wireless network design, the monograph is expected to offer a new and comprehensive methodology for the performance evaluation of large scale wireless networks. This methodology consists in the computation of both time and space averages within a unified setting which inherently addresses the scalability issue in that it poses the problems in an infinite domain/population case. For readers with a background in applied probability, this monograph is expected to provide a direct access to an emerging and fast growing branch of spatial stochastic modeling.
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MatlabToolboxforDimensionalityReductiondrtoolbox.rar
20190813MatlabToolboxforDimensionalityReductiondrtoolbox.rar 最近想详细研究下特征提取和降维相关的东西.. 在网上搜索了一下.查到了个降维的工具箱.感觉还不错..先分享一下~ 这个工具箱里把常见的降维方法几乎都涵盖了.. drtoolbox.rar ================================= Currently, the Matlab Toolbox for Dimensionality Reduction contains the following techniques: Principal Component Analysis Probabilistic PCA Factor Analysis Sammon mapping Linear Discriminant Analysis Multidimensional scaling Isomap Landmark Isomap Local Linear Embedding Laplacian Eigenmaps Hessian LLE Local Tangent Space Alignment Conformal Eigenmaps Maximum Variance Unfolding Landmark MVU Fast Maximum Variance Unfolding Kernel PCA Generalized Discriminant Analysis Diffusion maps Stochastic Neighbor Embedding Symmetric SNE new: tDistributed Stochastic Neighbor Embedding Neighborhood Preserving Embedding Locality Preserving Projection Linear Local Tangent Space Alignment Stochastic Proximity Embedding Multilayer autoencoders Local Linear Coordination Manifold charting Coordinated Factor Analysis new: Gaussian Process Latent Variable Model
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Brownian Motion and Stochastic Calculus
20181019Graduate Texts in Mathematics loannis Karatzas Steven E. Shreve Brownian Motion and Stochastic Calculus Second Edition
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Simulation of Dynamic Systems with MATLAB® and Simulink® 3rd Edition
20180920Continuoussystem simulation is an increasingly important tool for optimizing the performance of realworld systems. The book presents an integrated treatment of continuous simulation with all the background and essential prerequisites in one setting. It features updated chapters and two new sections on Black Swan and the Stochastic Information Packet (SIP) and Stochastic Library Units with Relationships Preserved (SLURP) Standard. The new edition includes basic concepts, mathematical tools, and the common principles of various simulation models for different phenomena, as well as an abundance of case studies, realworld examples, homework problems, and equations to develop a practical understanding of concepts.
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Fast Stochastic  MetaTrader 4脚本.zip
20190910The Indicator Fast Stochastic is a kind of George C. Lane’s stochastic oscillator.
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Stochastic Models, Information Theory, and Lie Groups Volume 2
20081027机器人状态估计经典理论
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Stochastic Models, Estimation, and Control 123卷
20180514Stochastic Models Estimation and Control 1,2,3卷，高清PDF，文字可选，是时候收藏一波了
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Stochastic Calculus An Introduction Through Theory and Exercises
20190406Stochastic Calculus: An Introduction Through Theory and Exercises by Paolo Baldi UTX 系列 本书全面介绍了随机微积分理论及其一些应用。它是关于该主题的唯一包含200多个完整习题答案的教科书。 Amazon.com 销量第一

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