下载 >  课程资源 >  C/C++ > Common algorithm assembly.

Common algorithm assembly.

数学计算经典书籍,主要矩阵论理论,曲线拟合,差值等算法
2018-04-16 上传大小:7.83MB
想读
分享
收藏 (1) 举报
A Common-Sense Guide to Data Structures and Algorithms 无水印pdf

A Common-Sense Guide to Data Structures and Algorithms 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 本资源转载自网络,如有侵权,请联系上传者或csdn删除

立即下载
Analysis of the DVB Common Scrambling Algorithm

The Common Scrambling Algorithm (CSA) is used to encrypt streams of video data in the Digital Video Broadcasting (DVB) system. The algorithm cascades a stream and a block cipher, apparently for a larger security margin.

立即下载
DVB BlueBook A011r1

DVB Common Scrambling Algorithm : Distribution Agreements

立即下载
libdvbcsa-1.0.1

a free implementation of the DVB Common Scrambling Algorithm with encryption and decryption capabilities.

立即下载
An elitist teaching-learning-based optimization algorithm

In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated.

立即下载
Guide to Inline Assembly.

This is meant to be an introduction to inline assembly under DJGPP.

立即下载
CrypTool1.4加密工具

CrypTool1.4加密工具 a very popular packet of cryptography tools,it encloses the most common used algorithm and protocols -a very popular packet of cryptography tool s, it encloses the most common algorithm used and p rotocols

立即下载
Differences

This add-in shows differences between two versions of the same assembly.

立即下载
Algorithm(1983).pdf

Algorithm ,Algorithm ,Algorithm ,Algorithm ,Algorithm ,Algorithm ,Algorithm ,Algorithm ,

立即下载
go programming

用汇编语言写go的函数,write go function in assembly.

立即下载
Algorithm Design solution pdf

Algorithm Design by Jon Kleinberg & Eva Tardos 配套的solution。答案完整,题号可以通过目录查看。 这本书和配套的solution 都是我之前学习的时候买的正版的pdf, 现在分享给大家。书的资源我是另外分享的,有需要的同鞋可以点进来看看。

立即下载
algorithm design》/《算法设计》的pdf版本的课件(slides)个人觉得挺有用的

《algorithm design》/《算法设计》的pdf版本的课件(slides)个人觉得挺有用的。讲的东西比较精简, 还有简明的伪代码, 有书的看这个可以当提纲看, 不想看书的也可以只看这个, 个人感觉基本上够用了

立即下载
Algorithm negotiation fail

解决java强加密问题,Algorithm negotiation fail JDk1.8的Strong encrytion的问题,摘要必须大于100个字节!

立即下载
Data Structures and Algorithm Analysis in Java(3rd) 无水印pdf

Data Structures and Algorithm Analysis in Java(3rd) 英文无水印pdf 第3版 pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 本资源转载自网络,如有侵权,请联系上传者或csdn删除

立即下载
The EM Algorithm and Extensions (2nd Edition)

刚找到的书,第二版的.. 【原书作者】: Geoffrey J. McLachlan, Thriyambakam Krishnan 【ISBN 】: ISBN-10: 0471201707 / ISBN-13: 978-0471201700 【页数 】:360 【开本 】 : 【出版社】 :Wiley-Interscience 【出版日期】:March 14, 2008 【文件格式】:DJVU(请去网上下载windjview阅读 【摘要或目录】: Review "...should be comprehensible to graduates with statistics as their major subject." (Quarterly of Applied Mathematics, Vol. LIX, No. 3, September 2001) --This text refers to the Hardcover edition. Book Description The EM Algorithm and Extensions remains the only single source to offer a complete and unified treatment of the theory, methodology, and applications of the EM algorithm. The highly applied area of statistics here outlined involves applications in regression, medical imaging, finite mixture analysis, robust statistical modeling, survival analysis, and repeated-measures designs, among other areas. The text includes newly added and updated results on convergence, and new discussion of categorical data, numerical differentiation, and variants of the EM algorithm. It also explores the relationship between the EM algorithm and the Gibbs sampler and Markov Chain Monte Carlo methods. About Authors Geoffrey J. McLachlan, PhD, DSc, is Professor of Statistics in the Department of Mathematics at The University of Queensland, Australia. A Fellow of the American Statistical Association and the Australian Mathematical Society, he has published extensively on his research interests, which include cluster and discriminant analyses, image analysis, machine learning, neural networks, and pattern recognition. Dr. McLachlan is the author or coauthor of Analyzing Microarray Gene Expression Data, Finite Mixture Models, and Discriminant Analysis and Statistical Pattern Recognition, all published by Wiley. Thriyambakam Krishnan, PhD, is Chief Statistical Architect, SYSTAT Software at Cranes Software International Limited in Bangalore, India. Dr. Krishnan has over forty-five years of research, teaching, consulting, and software development experience at the Indian Statistical Institute (ISI). His research interests include biostatistics, image analysis, pattern recognition, psychometry, and the EM algorithm. 目录 Preface to the Second Edition. Preface to the First Edition. List of Examples. 1. General Introduction. 1.1 Introduction. 1.2 Maximum Likelihood Estimation. 1.3 Newton-Type Methods. 1.4 Introductory Examples. 1.5 Formulation of the EM Algorithm. 1.6 EM Algorithm for MAP and MPL Estimation. 1.7 Brief Summary of the Properties of EM Algorithm. 1.8 History of the EM Algorithm. 1.9 Overview of the Book. 1.10 Notations. 2. Examples of the EM Algorithm. 2.1 Introduction. 2.2 Multivariate Data with Missing Values. 2.3 Least Square with the Missing Data. 2.4 Example 2.4: Multinomial with Complex Cell Structure. 2.5 Example 2.5: Analysis of PET and SPECT Data. 2.6 Example 2.6: Multivariate t-Distribution (Known D.F.). 2.7 Finite Normal Mixtures. 2.8 Example 2.9: Grouped and Truncated Data. 2.9 Example 2.10: A Hidden Markov AR(1) Model. 3. Basic Theory of the EM Algorithm. 3.1 Introduction. 3.2 Monotonicity of a Generalized EM Algorithm. 3.3 Monotonicity of a Generalized EM Algorithm. 3.4 Convergence of an EM Sequence to a Stationary Value. 3.5 Convergence of an EM Sequence of Iterates. 3.6 Examples of Nontypical Behavior of an EM (GEM) Sequence. 3.7 Score Statistic. 3.8 Missing Information. 3.9 Rate of Convergence of the EM Algorithm. 4. Standard Errors and Speeding up Convergence. 4.1 Introduction. 4.2 Observed Information Matrix. 4.3 Approximations to Observed Information Matrix: i.i.d. Case. 4.4 Observed Information Matrix for Grouped Data. 4.5 Supplemented EM Algorithm. 4.6 Bookstrap Approach to Standard Error Approximation. 4.7 Baker’s, Louis’, and Oakes’ Methods for Standard Error Computation. 4.8 Acceleration of the EM Algorithm via Aitken’s Method. 4.9 An Aitken Acceleration-Based Stopping Criterion. 4.10 conjugate Gradient Acceleration of EM Algorithm. 4.11 Hybrid Methods for Finding the MLE. 4.12 A GEM Algorithm Based on One Newton-Raphson Algorithm. 4.13 EM gradient Algorithm. 4.14 A Quasi-Newton Acceleration of the EM Algorithm. 4.15 Ikeda Acceleration. 5. Extension of the EM Algorithm. 5.1 Introduction. 5.2 ECM Algorithm. 5.3 Multicycle ECM Algorithm. 5.4 Example 5.2: Normal Mixtures with Equal Correlations. 5.5 Example 5.3: Mixture Models for Survival Data. 5.6 Example 5.4: Contingency Tables with Incomplete Data. 5.7 ECME Algorithm. 5.8 Example 5.5: MLE of t-Distribution with the Unknown D.F. 5.9 Example 5.6: Variance Components. 5.10 Linear Mixed Models. 5.11 Example 5.8: Factor Analysis. 5.12 Efficient Data Augmentation. 5.13 Alternating ECM Algorithm. 5.14 Example 5.9: Mixtures of Factor Analyzers. 5.15 Parameter-Expanded EM (PX-EM) Algorithm. 5.16 EMS Algorithm. 5.17 One-Step-Late Algorithm. 5.18 Variance Estimation for Penalized EM and OSL Algorithms. 5.19 Incremental EM. 5.20 Linear Inverse problems. 6. Monte Carlo Versions of the EM Algorithm. 6.1 Introduction. 6.2 Monte Carlo Techniques. 6.3 Monte Carlo EM. 6.4 Data Augmentation. 6.5 Bayesian EM. 6.6 I.I.D. Monte Carlo Algorithm. 6.7 Markov Chain Monte Carlo Algorithms. 6.8 Gibbs Sampling. 6.9 Examples of MCMC Algorithms. 6.10 Relationship of EM to Gibbs Sampling. 6.11 Data Augmentation and Gibbs Sampling. 6.12 Empirical Bayes and EM. 6.13 Multiple Imputation. 6.14 Missing-Data Mechanism, Ignorability, and EM Algorithm. 7. Some Generalization of the EM Algorithm. 7.1 Introduction. 7.2 Estimating Equations and Estimating Functions. 7.3 Quasi-Score and the Projection-Solution Algorithm. 7.4 Expectation-Solution (ES) Algorithm. 7.5 Other Generalization. 7.6 Variational Bayesian EM Algorithm. 7.7 MM Algorithm. 7.8 Lower Bound Maximization. 7.9 Interval EM Algorithm. 7.10 Competing Methods and Some Comparisons with EM. 7.11 The Delta Algorithm. 7.12 Image Space Reconstruction Algorithm. 8. Further Applications of the EM Algorithm. 8.1 Introduction. 8.2 Hidden Markov Models. 8.3 AIDS Epidemiology. 8.4 Neural Networks. 8.5 Data Mining. 8.6 Bioinformatics. References. Author Index. Subject Index

立即下载
The.Algorithm.Design.Manual中文版+英文原版

数据结构与算法是程序员必须掌握的一门课程,本书作为国外经典教材。自从出版以来,深受大家的喜欢。

立即下载
Algorithm Design 书后习题全解,附有打开pdf的密码

Algorithm Design 书后习题全解,压缩包中附有打开pdf的密码

立即下载
c#编写数值算法源代码

c#常用数值算法的源代码,包含插值、矩阵、线性方程组等算法源码,非常有益于开发-c# common numerical algorithm s source code, including interpolation, matrix, linear equations, such as algorithm source code, very useful in the development of

立即下载
Common 类 Common 类

很多 通用的类和方法很多 通用的类和方法很多 通用的类和方法很多 通用的类和方法很多 通用的类和方法很多 通用的类和方法很多 通用的类和方法

立即下载
common

common com 课件

立即下载
关闭
img

spring mvc+mybatis+mysql+maven+bootstrap 整合实现增删查改简单实例.zip

资源所需积分/C币 当前拥有积分 当前拥有C币
5 0 0
点击完成任务获取下载码
输入下载码
为了良好体验,不建议使用迅雷下载
img

Common algorithm assembly.

会员到期时间: 剩余下载个数: 剩余C币: 剩余积分:0
为了良好体验,不建议使用迅雷下载
VIP下载
您今日下载次数已达上限(为了良好下载体验及使用,每位用户24小时之内最多可下载20个资源)

积分不足!

资源所需积分/C币 当前拥有积分
您可以选择
开通VIP
4000万
程序员的必选
600万
绿色安全资源
现在开通
立省522元
或者
购买C币兑换积分 C币抽奖
img

资源所需积分/C币 当前拥有积分 当前拥有C币
5 4 45
为了良好体验,不建议使用迅雷下载
确认下载
img

资源所需积分/C币 当前拥有积分 当前拥有C币
5 0 0
为了良好体验,不建议使用迅雷下载
VIP和C币套餐优惠
img

资源所需积分/C币 当前拥有积分 当前拥有C币
5 4 45
您的积分不足,将扣除 10 C币
为了良好体验,不建议使用迅雷下载
确认下载
下载
您还未下载过该资源
无法举报自己的资源

兑换成功

你当前的下载分为234开始下载资源
你还不是VIP会员
开通VIP会员权限,免积分下载
立即开通

你下载资源过于频繁,请输入验证码

您因违反CSDN下载频道规则而被锁定帐户,如有疑问,请联络:webmaster@csdn.net!

举报

若举报审核通过,可返还被扣除的积分

  • 举报人:
  • 被举报人:
  • *类型:
    • *投诉人姓名:
    • *投诉人联系方式:
    • *版权证明:
  • *详细原因: