東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Statistical foundations, reasoning a...

Kauermann, Goran.

FindBook

Google Book

Amazon

博客來

Statistical foundations, reasoning and inference = for science and data science /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical foundations, reasoning and inference/ by Goran Kauermann, Helmut Kuchenhoff, Christian Heumann.
其他題名:	for science and data science /
作者:	Kauermann, Goran.
其他作者:	Kuchenhoff, Helmut.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xiii, 356 p. :ill., digital ;24 cm.
內容註:	Introduction -- Background in Probability -- Parametric Statistical Models -- Maximum Likelihood Inference -- Bayesian Statistics -- Statistical Decisions -- Regression -- Bootstrapping -- Model Selection and Model Averaging -- Multivariate and Extreme Value Distributions -- Missing and Deficient Data -- Experiments and Causality.
Contained By:	Springer Nature eBook
標題:	Mathematical statistics. -
電子資源:	https://doi.org/10.1007/978-3-030-69827-0
ISBN:	9783030698270

Statistical foundations, reasoning and inference = for science and data science /
Kauermann, Goran.

Statistical foundations, reasoning and inferencefor science and data science /[electronic resource] :by Goran Kauermann, Helmut Kuchenhoff, Christian Heumann. - Cham :Springer International Publishing :2021. - xiii, 356 p. :ill., digital ;24 cm. - Springer series in statistics,2197-568X. - Springer series in statistics..

Introduction -- Background in Probability -- Parametric Statistical Models -- Maximum Likelihood Inference -- Bayesian Statistics -- Statistical Decisions -- Regression -- Bootstrapping -- Model Selection and Model Averaging -- Multivariate and Extreme Value Distributions -- Missing and Deficient Data -- Experiments and Causality.

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master's students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.

ISBN: 9783030698270

Standard No.: 10.1007/978-3-030-69827-0doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Statistical foundations, reasoning and inference = for science and data science /
LDR:02262nmm a2200337 a 4500 001 2251483
003 DE-He213
005 20210930191909.0
006 m d
007 cr nn 008maaau
008 220215s2021 sz s 0 eng d
020 $a 9783030698270 $q (electronic bk.)
020 $a 9783030698263 $q (paper)
024 7 $a 10.1007/978-3-030-69827-0 $2 doi
035 $a 978-3-030-69827-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .K21 2021
100 1 $a Kauermann, Goran. $3 3518535
245 1 0 $a Statistical foundations, reasoning and inference $h [electronic resource] : $b for science and data science / $c by Goran Kauermann, Helmut Kuchenhoff, Christian Heumann.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiii, 356 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in statistics, $x 2197-568X
505 0 $a Introduction -- Background in Probability -- Parametric Statistical Models -- Maximum Likelihood Inference -- Bayesian Statistics -- Statistical Decisions -- Regression -- Bootstrapping -- Model Selection and Model Averaging -- Multivariate and Extreme Value Distributions -- Missing and Deficient Data -- Experiments and Causality.
520 $a This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master's students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.
650 0 $a Mathematical statistics. $3 516858
650 1 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Data Structures and Information Theory. $3 3382368
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
700 1 $a Kuchenhoff, Helmut. $3 3518536
700 1 $a Heumann, Christian. $3 907896
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer series in statistics. $3 1315057
856 4 0 $u https://doi.org/10.1007/978-3-030-69827-0
950 $a Mathematics and Statistics (SpringerNature-11649)