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Statistical modeling and computation [electronic resource]

Statistical modeling and computation [electronic resource]

Material type
E-Book(소장)
Personal Author
Kroese, Dirk P. Chan, Joshua (Joshua C. C.).
Title Statement
Statistical modeling and computation [electronic resource] / Dirk P. Kroese, Joshua C.C. Chan.
Publication, Distribution, etc
New York, NY :   Springer New York :   Imprint: Springer,   2014.  
Physical Medium
1 online resource (xx, 400 p.) : ill. (some col.).
ISBN
9781461487753
요약
This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to university courses. Part I covers the fundamentals of probability theory. In Part II, the authors introduce a wide variety of classical models that include, among others, linear regression and ANOVA models. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models. Particular attention is paid to fast Monte Carlo techniques for Bayesian inference on these models. Throughout the book the authors include a large number of illustrative examples and solved problems. The book also features a section with solutions, an appendix that serves as a MATLAB primer, and a mathematical supplement.
General Note
Title from e-Book title page.  
Content Notes
Probability Models -- Random Variables and Probability Distributions -- Joint Distributions -- Common Statistical Models -- Statistical Inference -- Likelihood -- Monte Carlo Sampling -- Bayesian Inference -- Generalized Linear Models -- Dependent Data Models -- State Space Models -- References -- Solutions -- MATLAB Primer -- Mathematical Supplement -- Index.
Bibliography, Etc. Note
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
Subject Added Entry-Topical Term
Statistics. Probabilities. Mathematical models.
Short cut
URL
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001 000046042519
005 20200831153156
006 m d
007 cr
008 200814s2014 nyua ob 001 0 eng d
020 ▼a 9781461487753
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 001.4 ▼2 23
084 ▼a 001.4 ▼2 DDCK
090 ▼a 001.4
100 1 ▼a Kroese, Dirk P.
245 1 0 ▼a Statistical modeling and computation ▼h [electronic resource] / ▼c Dirk P. Kroese, Joshua C.C. Chan.
260 ▼a New York, NY : ▼b Springer New York : ▼b Imprint: Springer, ▼c 2014.
300 ▼a 1 online resource (xx, 400 p.) : ▼b ill. (some col.).
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a Probability Models -- Random Variables and Probability Distributions -- Joint Distributions -- Common Statistical Models -- Statistical Inference -- Likelihood -- Monte Carlo Sampling -- Bayesian Inference -- Generalized Linear Models -- Dependent Data Models -- State Space Models -- References -- Solutions -- MATLAB Primer -- Mathematical Supplement -- Index.
520 ▼a This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to university courses. Part I covers the fundamentals of probability theory. In Part II, the authors introduce a wide variety of classical models that include, among others, linear regression and ANOVA models. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models. Particular attention is paid to fast Monte Carlo techniques for Bayesian inference on these models. Throughout the book the authors include a large number of illustrative examples and solved problems. The book also features a section with solutions, an appendix that serves as a MATLAB primer, and a mathematical supplement.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
630 0 0 ▼a MATLAB.
650 0 ▼a Statistics.
650 0 ▼a Probabilities.
650 0 ▼a Mathematical models.
700 1 ▼a Chan, Joshua ▼q (Joshua C. C.).
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-1-4614-8775-3
945 ▼a KLPA
991 ▼a E-Book(소장)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 001.4 Accession No. E14031236 Availability Loan can not(reference room) Due Date Make a Reservation Service M

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