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Techniques of Variational Analysis下载
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2019-05-08 10:00:15
Jonathan M. Borwein Qiji J. Zhu
Techniques of
Variational Analysis
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Techniques of Variational Analysis下载
Jonathan M. Borwein Qiji J. Zhu Techniques of Variational Analysis 相关下载链接://download.csdn.net/download/kakaiverson/2134198?utm_source=bbsseo
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Techniques
of
Var
iat
ion
al
An
al
ysis
Jonathan M. Borwein Qiji J. Zhu
Techniques
of
Var
iat
ion
al
An
al
ysis
Var
iat
ion
al
B-Spline Level-Set
In the field of image segmentat
ion
, most level-setbased active-contour approaches take advantage of a discrete representat
ion
of the assoc
iat
ed implicit funct
ion
. We present in this paper a different formulat
ion
where the implicit funct
ion
is modeled as a continuous parametric funct
ion
expressed on a B-spline basis. Starting from the active-contour energy funct
ion
al
, we show that this formulat
ion
al
lows us to compute the solut
ion
as a restrict
ion
of the
var
iat
ion
al
problem on the space spanned by the B-splines.
The EM
Al
gorithm 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-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 edit
ion
. Book Descript
ion
The EM
Al
gorithm and Extens
ion
s remains the only single source to offer a complete and unified treatment of the theory, methodology, and applicat
ion
s of the EM
al
gorithm. The highly applied area of statistics here outlined involves applicat
ion
s in regress
ion
, medic
al
imaging, finite mixture
an
al
ysis
, robust statistic
al
modeling, surviv
al
an
al
ysis
, and repeated-measures designs, among other areas. The text includes newly added and updated results on convergence, and new discuss
ion
of categoric
al
data, numeric
al
different
iat
ion
, and
var
iants of the EM
al
gorithm. It
al
so explores the relat
ion
ship between the EM
al
gorithm 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, Austr
al
ia. A Fellow of the American Statistic
al
Assoc
iat
ion
and the Austr
al
ian Mathematic
al
Society, he has published extensively on his research interests, which include cluster and discriminant an
al
yses, image
an
al
ysis
, machine learning, neur
al
networks, and pattern recognit
ion
. Dr. McLachlan is the author or coauthor of An
al
yzing Microarray Gene Express
ion
Data, Finite Mixture Models, and Discriminant
An
al
ysis
and Statistic
al
Pattern Recognit
ion
,
al
l published by Wiley. Thriyambakam Krishnan, PhD, is Chief Statistic
al
Architect, SYSTAT Software at Cranes Software Internat
ion
al
Limited in Bang
al
ore, India. Dr. Krishnan has over forty-five years of research, teaching, consulting, and software development experience at the Indian Statistic
al
Institute (ISI). His research interests include biostatistics, image
an
al
ysis
, pattern recognit
ion
, psychometry, and the EM
al
gorithm. 目录 Preface to the Second Edit
ion
. Preface to the First Edit
ion
. List of Examples. 1. Gener
al
Introduct
ion
. 1.1 Introduct
ion
. 1.2 Maximum Likelihood Estimat
ion
. 1.3 Newton-Type Methods. 1.4 Introductory Examples. 1.5 Formulat
ion
of the EM
Al
gorithm. 1.6 EM
Al
gorithm for MAP and MPL Estimat
ion
. 1.7 Brief Summary of the Properties of EM
Al
gorithm. 1.8 History of the EM
Al
gorithm. 1.9 Overview of the Book. 1.10 Notat
ion
s. 2. Examples of the EM
Al
gorithm. 2.1 Introduct
ion
. 2.2 Multi
var
iat
e Data with Missing V
al
ues. 2.3 Least Square with the Missing Data. 2.4 Example 2.4: Multinomi
al
with Complex Cell Structure. 2.5 Example 2.5:
An
al
ysis
of PET and SPECT Data. 2.6 Example 2.6: Multi
var
iat
e t-Distribut
ion
(Known D.F.). 2.7 Finite Norm
al
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
Al
gorithm. 3.1 Introduct
ion
. 3.2 Monotonicity of a Gener
al
ized EM
Al
gorithm. 3.3 Monotonicity of a Gener
al
ized EM
Al
gorithm. 3.4 Convergence of an EM Sequence to a Stat
ion
ary V
al
ue. 3.5 Convergence of an EM Sequence of Iterates. 3.6 Examples of Nontypic
al
Behavior of an EM (GEM) Sequence. 3.7 Score Statistic. 3.8 Missing Informat
ion
. 3.9 Rate of Convergence of the EM
Al
gorithm. 4. Standard Errors and Speeding up Convergence. 4.1 Introduct
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
Al
gorithm. 4.6 Bookstrap Approach to Standard Error Approximat
ion
. 4.7 Baker’s, Louis’, and Oakes’ Methods for Standard Error Computat
ion
. 4.8 Accelerat
ion
of the EM
Al
gorithm via Aitken’s Method. 4.9 An Aitken Accelerat
ion
-Based Stopping Criter
ion
. 4.10 conjugate Gradient Accelerat
ion
of EM
Al
gorithm. 4.11 Hybrid Methods for Finding the MLE. 4.12 A GEM
Al
gorithm Based on One Newton-Raphson
Al
gorithm. 4.13 EM gradient
Al
gorithm. 4.14 A Quasi-Newton Accelerat
ion
of the EM
Al
gorithm. 4.15 Ikeda Accelerat
ion
. 5. Extens
ion
of the EM
Al
gorithm. 5.1 Introduct
ion
. 5.2 ECM
Al
gorithm. 5.3 Multicycle ECM
Al
gorithm. 5.4 Example 5.2: Norm
al
Mixtures with Equ
al
Correlat
ion
s. 5.5 Example 5.3: Mixture Models for Surviv
al
Data. 5.6 Example 5.4: Contingency Tables with Incomplete Data. 5.7 ECME
Al
gorithm. 5.8 Example 5.5: MLE of t-Distribut
ion
with the Unknown D.F. 5.9 Example 5.6:
Var
iance Components. 5.10 Linear Mixed Models. 5.11 Example 5.8: Factor
An
al
ysis
. 5.12 Efficient Data Augmentat
ion
. 5.13
Al
ternating ECM
Al
gorithm. 5.14 Example 5.9: Mixtures of Factor An
al
yzers. 5.15 Parameter-Expanded EM (PX-EM)
Al
gorithm. 5.16 EMS
Al
gorithm. 5.17 One-Step-Late
Al
gorithm. 5.18
Var
iance Estimat
ion
for Pen
al
ized EM and OSL
Al
gorithms. 5.19 Increment
al
EM. 5.20 Linear Inverse problems. 6. Monte Carlo Vers
ion
s of the EM
Al
gorithm. 6.1 Introduct
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
Al
gorithm. 6.7 Markov Chain Monte Carlo
Al
gorithms. 6.8 Gibbs Sampling. 6.9 Examples of MCMC
Al
gorithms. 6.10 Relat
ion
ship of EM to Gibbs Sampling. 6.11 Data Augmentat
ion
and Gibbs Sampling. 6.12 Empiric
al
Bayes and EM. 6.13 Multiple Imputat
ion
. 6.14 Missing-Data Mechanism, Ignorability, and EM
Al
gorithm. 7. Some Gener
al
izat
ion
of the EM
Al
gorithm. 7.1 Introduct
ion
. 7.2 Estimating Equat
ion
s and Estimating Funct
ion
s. 7.3 Quasi-Score and the Project
ion
-Solut
ion
Al
gorithm. 7.4 Expectat
ion
-Solut
ion
(ES)
Al
gorithm. 7.5 Other Gener
al
izat
ion
. 7.6
Var
iat
ion
al
Bayesian EM
Al
gorithm. 7.7 MM
Al
gorithm. 7.8 Lower Bound Maximizat
ion
. 7.9 Interv
al
EM
Al
gorithm. 7.10 Competing Methods and Some Comparisons with EM. 7.11 The Delta
Al
gorithm. 7.12 Image Space Reconstruct
ion
Al
gorithm. 8. Further Applicat
ion
s of the EM
Al
gorithm. 8.1 Introduct
ion
. 8.2 Hidden Markov Models. 8.3 AIDS Epidemiology. 8.4 Neur
al
Networks. 8.5 Data Mining. 8.6 Bioinformatics. References. Author Index. Subject Index
Informat
ion
Theory, Inference, and Learning
Al
gorithms David J.C. MacKay
信息论,推断与学习理论 英文原版 Informat
ion
theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communicat
ion
, sign
al
processing, data mining, machine learning, pattern recognit
ion
, computat
ion
al
neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applicat
ion
s. Informat
ion
theory is taught
al
ongside practic
al
communicat
ion
systems, such as arithmetic coding for data compress
ion
and sparse-graph codes for error-correct
ion
. A toolbox of inference
techniques
, including message-passing
al
gorithms, Monte Carlo methods, and
var
iat
ion
al
approximat
ion
s, are developed
al
ongside applicat
ion
s of these tools to clustering, convolut
ion
al
codes, independent component
an
al
ysis
, and neur
al
networks. The fin
al
part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digit
al
fountain codes -- the twenty-first century standards for satellite communicat
ion
s, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solut
ion
s, David MacKay's groundbreaking book is ide
al
for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolut
ion
, and sex provide entertainment
al
ong the way. In sum, this is a textbook on informat
ion
, communicat
ion
, and coding for a new generat
ion
of students, and an unpar
al
leled entry point into these subjects for profess
ion
al
s in areas as diverse as computat
ion
al
biology, financi
al
engineering, and machine learning
Bishop Pattern Recognit
ion
and Machine Learning
Bishop Pattern Recognit
ion
and Machine Learning
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