國立高雄大學圖資館 |

Language: English

Back

Statistical methods for data analysi...

Lista, Luca.

Statistical methods for data analysis in particle physics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical methods for data analysis in particle physicsby Luca Lista.
Author:	Lista, Luca.
Published:	Cham :Springer International Publishing :2017.
Description:	xvi, 257 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Nuclear physicsStatistical methods.
Online resource:	http://dx.doi.org/10.1007/978-3-319-62840-0
ISBN:	9783319628400$q(electronic bk.)

Statistical methods for data analysis in particle physics
Lista, Luca.

Statistical methods for data analysis in particle physics[electronic resource] /by Luca Lista. - 2nd ed. - Cham :Springer International Publishing :2017. - xvi, 257 p. :ill. (some col.), digital ;24 cm. - Lecture notes in physics,v.9410075-8450 ;. - Lecture notes in physics ;650..

Preface -- Probability theory -- Probability Distribution Functions -- Bayesian Approach to Probability -- Random Numbers and Monte Carlo Methods -- Parameter Estimate -- Combining Measurements -- Confidence Intervals -- Convolution and Unfolding -- Hypothesis Tests -- Discoveries and Upper Limits -- Index.

This concise set of course-based notes provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP). First, the book provides an introduction to probability theory and basic statistics, mainly intended as a refresher from readers' advanced undergraduate studies, but also to help them clearly distinguish between the Frequentist and Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on both discoveries and upper limits, as many applications in HEP concern hypothesis testing, where the main goal is often to provide better and better limits so as to eventually be able to distinguish between competing hypotheses, or to rule out some of them altogether. Many worked-out examples will help newcomers to the field and graduate students alike understand the pitfalls involved in applying theoretical concepts to actual data. This new second edition significantly expands on the original material, with more background content (e.g. the Markov Chain Monte Carlo method, best linear unbiased estimator), applications (unfolding and regularization procedures, control regions and simultaneous fits, machine learning concepts) and examples (e.g. look-elsewhere effect calculation)

ISBN: 9783319628400$q(electronic bk.)

Standard No.: 10.1007/978-3-319-62840-0doiSubjects--Topical Terms:

736774
Nuclear physics
--Statistical methods.

LC Class. No.: QC793.47.S83

Dewey Class. No.: 539.720727

Statistical methods for data analysis in particle physics
LDR:02742nmm a2200337 a 4500 001 523728
003 DE-He213
005 20180430135808.0
006 m d
007 cr nn 008maaau
008 180628s2017 gw s 0 eng d
020 $a 9783319628400$q(electronic bk.)
020 $a 9783319628394$q(paper)
024 7 $a 10.1007/978-3-319-62840-0 $2 doi
035 $a 978-3-319-62840-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QC793.47.S83
072 7 $a PHQ $2 bicssc
072 7 $a SCI051000 $2 bisacsh
082 0 4 $a 539.720727 $2 23
090 $a QC793.47.S83 $b L773 2017
100 1 $a Lista, Luca. $3 736773
245 1 0 $a Statistical methods for data analysis in particle physics $h [electronic resource] / $c by Luca Lista.
250 $a 2nd ed.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xvi, 257 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in physics, $x 0075-8450 ; $v v.941
505 0 $a Preface -- Probability theory -- Probability Distribution Functions -- Bayesian Approach to Probability -- Random Numbers and Monte Carlo Methods -- Parameter Estimate -- Combining Measurements -- Confidence Intervals -- Convolution and Unfolding -- Hypothesis Tests -- Discoveries and Upper Limits -- Index.
520 $a This concise set of course-based notes provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP). First, the book provides an introduction to probability theory and basic statistics, mainly intended as a refresher from readers' advanced undergraduate studies, but also to help them clearly distinguish between the Frequentist and Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on both discoveries and upper limits, as many applications in HEP concern hypothesis testing, where the main goal is often to provide better and better limits so as to eventually be able to distinguish between competing hypotheses, or to rule out some of them altogether. Many worked-out examples will help newcomers to the field and graduate students alike understand the pitfalls involved in applying theoretical concepts to actual data. This new second edition significantly expands on the original material, with more background content (e.g. the Markov Chain Monte Carlo method, best linear unbiased estimator), applications (unfolding and regularization procedures, control regions and simultaneous fits, machine learning concepts) and examples (e.g. look-elsewhere effect calculation)
650 0 $a Nuclear physics $x Statistical methods. $3 736774
650 1 4 $a Physics. $3 179414
650 2 4 $a Elementary Particles, Quantum Field Theory. $3 274994
650 2 4 $a Measurement Science and Instrumentation. $3 376366
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 348605
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Lecture notes in physics ; $v 650. $3 451204
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-62840-0
950 $a Physics and Astronomy (Springer-11651)