國立虎尾科技大學 |

Identifiability and Regression Analysis of Biological Systems Models = Statistical and Mathematical Foundations and R Scripts /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Identifiability and Regression Analysis of Biological Systems Models/ by Paola Lecca.
其他題名:	Statistical and Mathematical Foundations and R Scripts /
作者:	Lecca, Paola.
面頁冊數:	X, 82 p. 13 illus., 8 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. -
電子資源:	https://doi.org/10.1007/978-3-030-41255-5
ISBN:	9783030412555

Identifiability and Regression Analysis of Biological Systems Models = Statistical and Mathematical Foundations and R Scripts /
Lecca, Paola.

Identifiability and Regression Analysis of Biological Systems ModelsStatistical and Mathematical Foundations and R Scripts /[electronic resource] :by Paola Lecca. - 1st ed. 2020. - X, 82 p. 13 illus., 8 illus. in color.online resource. - SpringerBriefs in Statistics,2191-544X. - SpringerBriefs in Statistics,0.

1 Complex systems and sets of data -- 2 Dynamic models -- 3 Model identifiability -- 4 Relationships between phenomena -- 5 Codes.

This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection. Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting their own regression analysis and diagnostics without supervision. Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R.

ISBN: 9783030412555

Standard No.: 10.1007/978-3-030-41255-5doiSubjects--Topical Terms:

782247
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Identifiability and Regression Analysis of Biological Systems Models = Statistical and Mathematical Foundations and R Scripts /
LDR:03055nam a22004095i 4500 001 1019585
003 DE-He213
005 20201218012957.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030412555 $9 978-3-030-41255-5
024 7 $a 10.1007/978-3-030-41255-5 $2 doi
035 $a 978-3-030-41255-5
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MED090000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a MBNS $2 thema
082 0 4 $a 519.5 $2 23
100 1 $a Lecca, Paola. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1074326
245 1 0 $a Identifiability and Regression Analysis of Biological Systems Models $h [electronic resource] : $b Statistical and Mathematical Foundations and R Scripts / $c by Paola Lecca.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a X, 82 p. 13 illus., 8 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Statistics, $x 2191-544X
505 0 $a 1 Complex systems and sets of data -- 2 Dynamic models -- 3 Model identifiability -- 4 Relationships between phenomena -- 5 Codes.
520 $a This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection. Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting their own regression analysis and diagnostics without supervision. Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R.
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 782247
650 2 4 $a Mathematical and Computational Biology. $3 786706
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Systems Biology. $3 683756
650 1 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
650 0 $a Biomathematics. $3 527725
650 0 $a Biostatistics. $3 783654
650 0 $a Systems biology. $3 600045
650 0 $a Statistics . $3 1253516
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030412548
776 0 8 $i Printed edition: $z 9783030412562
830 0 $a SpringerBriefs in Statistics, $x 2191-544X ; $v 0 $3 1254767
856 4 0 $u https://doi.org/10.1007/978-3-030-41255-5
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)