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Introduction to The Theory of Computation(Second Edition)下载
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2019-05-27 07:30:16
Introduction to The Theory of Computation
Second Edition
英文版
MICHAEL SIPSER
DJVU格式
内附DJVU格式阅读器(绿色版)
相关下载链接:
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Introduction to The Theory of Computation(Second Edition)下载
Introduction to The Theory of Computation Second Edition 英文版 MICHAEL SIPSER DJVU格式 内附DJVU格式阅读器(绿色版) 相关下载链接://download.csdn.net/download/denlee/2364736?utm_source=bbsseo
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Int
roduct
ion
to The
Theory
of
Com
putat
ion
(
Second
Edit
ion
)
Int
roduct
ion
to The
Theory
of
Com
putat
ion
Second
Edit
ion
英文版 MICHAEL SIPSER DJVU格式 内附DJVU格式阅读器(绿色版)
Int
roduct
ion
to the
Theory
of
Com
putat
ion
,
Second
Edit
ion
, pdf
Int
roduct
ion
to the
Theory
of
Com
putat
ion
,
Second
Edit
ion
, pdf 计算理论导论 第二版 電子書
Int
roduct
ion
to Automata
Theory
, Languages, and
Com
putat
ion
(
Second
Edit
ion
)中文答案
是
Int
roduct
ion
to Automata
Theory
, Languages, and
Com
putat
ion
(
Second
Edit
ion
)书的课后习题中文答案
Addison-Wesley -
Int
roduct
ion
to Automata
Theory
, Languages and
Com
putat
ion
Second
Edit
ion
by Hopcroft, Motwani and Ullman.pdf
Addison-Wesley -
Int
roduct
ion
to Automata
Theory
, Languages and
Com
putat
ion
Second
Edit
ion
by Hopcroft, Motwani and Ullman
The EM Algorithm and Extens
ion
s (2nd
Edit
ion
)
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-
Int
erscience 【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
com
prehensible to graduates with statistics as their major subject." (Quarterly of Applied Mathematics, Vol. LIX, No. 3, September 2001) --This text refers to the Hardcover
edit
ion
. Book Descript
ion
The EM Algorithm and Extens
ion
s remains the only single source to offer a
com
plete and unified treatment of the
theory
, methodology, and applicat
ion
s of the EM algorithm. The highly applied area of statistics here outlined involves applicat
ion
s in regress
ion
, 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 discuss
ion
of categorical data, numerical differentiat
ion
, and variants of the EM algorithm. It also explores the relat
ion
ship 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 Associat
ion
and the Australian Mathematical Society, he has published extensively on his research
int
erests, which include cluster and discriminant analyses, image analysis, machine learning, neural networks, and pattern recognit
ion
. Dr. McLachlan is the author or coauthor of Analyzing Microarray Gene Express
ion
Data, Finite Mixture Models, and Discriminant Analysis and Statistical Pattern Recognit
ion
, all published by Wiley. Thriyambakam Krishnan, PhD, is Chief Statistical Architect, SYSTAT Software at Cranes Software
Int
ernat
ion
al 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
int
erests include biostatistics, image analysis, pattern recognit
ion
, psychometry, and the EM algorithm. 目录 Preface to the
Second
Edit
ion
. Preface to the First
Edit
ion
. List of Examples. 1. General
Int
roduct
ion
. 1.1
Int
roduct
ion
. 1.2 Maximum Likelihood Estimat
ion
. 1.3 Newton-Type Methods. 1.4
Int
roduct
ory Examples. 1.5 Formulat
ion
of the EM Algorithm. 1.6 EM Algorithm for MAP and MPL Estimat
ion
. 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 Notat
ion
s. 2. Examples of the EM Algorithm. 2.1
Int
roduct
ion
. 2.2 Multivariate Data with Missing Values. 2.3 Least Square with the Missing Data. 2.4 Example 2.4: Multinomial with
Com
plex Cell Structure. 2.5 Example 2.5: Analysis of PET and SPECT Data. 2.6 Example 2.6: Multivariate t-Distribut
ion
(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
Int
roduct
ion
. 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 Stat
ion
ary 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 Informat
ion
. 3.9 Rate of Convergence of the EM Algorithm. 4. Standard Errors and Speeding up Convergence. 4.1
Int
roduct
ion
. 4.2 Observed Informat
ion
Matrix. 4.3 Approximat
ion
s to Observed Informat
ion
Matrix: i.i.d. Case. 4.4 Observed Informat
ion
Matrix for Grouped Data. 4.5 Supplemented EM Algorithm. 4.6 Bookstrap Approach to Standard Error Approximat
ion
. 4.7 Baker’s, Louis’, and Oakes’ Methods for Standard Error
Com
putat
ion
. 4.8 Accelerat
ion
of the EM Algorithm via Aitken’s Method. 4.9 An Aitken Accelerat
ion
-Based Stopping Criter
ion
. 4.10 conjugate Gradient Accelerat
ion
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 Accelerat
ion
of the EM Algorithm. 4.15 Ikeda Accelerat
ion
. 5. Extens
ion
of the EM Algorithm. 5.1
Int
roduct
ion
. 5.2 ECM Algorithm. 5.3 Multicycle ECM Algorithm. 5.4 Example 5.2: Normal Mixtures with Equal Correlat
ion
s. 5.5 Example 5.3: Mixture Models for Survival Data. 5.6 Example 5.4: Contingency Tables with In
com
plete Data. 5.7 ECME Algorithm. 5.8 Example 5.5: MLE of t-Distribut
ion
with the Unknown D.F. 5.9 Example 5.6: Variance
Com
ponents. 5.10 Linear Mixed Models. 5.11 Example 5.8: Factor Analysis. 5.12 Efficient Data Augmentat
ion
. 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 Estimat
ion
for Penalized EM and OSL Algorithms. 5.19 Incremental EM. 5.20 Linear Inverse problems. 6. Monte Carlo Vers
ion
s of the EM Algorithm. 6.1
Int
roduct
ion
. 6.2 Monte Carlo Techniques. 6.3 Monte Carlo EM. 6.4 Data Augmentat
ion
. 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 Relat
ion
ship of EM to Gibbs Sampling. 6.11 Data Augmentat
ion
and Gibbs Sampling. 6.12 Empirical Bayes and EM. 6.13 Multiple Imputat
ion
. 6.14 Missing-Data Mechanism, Ignorability, and EM Algorithm. 7. Some Generalizat
ion
of the EM Algorithm. 7.1
Int
roduct
ion
. 7.2 Estimating Equat
ion
s and Estimating Funct
ion
s. 7.3 Quasi-Score and the Project
ion
-Solut
ion
Algorithm. 7.4 Expectat
ion
-Solut
ion
(ES) Algorithm. 7.5 Other Generalizat
ion
. 7.6 Variat
ion
al Bayesian EM Algorithm. 7.7 MM Algorithm. 7.8 Lower Bound Maximizat
ion
. 7.9
Int
erval EM Algorithm. 7.10
Com
peting Methods and Some
Com
parisons with EM. 7.11 The Delta Algorithm. 7.12 Image Space Reconstruct
ion
Algorithm. 8. Further Applicat
ion
s of the EM Algorithm. 8.1
Int
roduct
ion
. 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
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