國立高雄大學圖資館 |

Language: English

Back

Uncertaintythe soul of modeling, pro...

Briggs, William.

Uncertaintythe soul of modeling, probability & statistics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Uncertaintyby William Briggs.
Reminder of title:	the soul of modeling, probability & statistics /
Author:	Briggs, William.
Published:	Cham :Springer International Publishing :2016.
Description:	xix, 258 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	UncertaintyMathematical models.
Online resource:	https://doi.org/10.1007/978-3-319-39756-6
ISBN:	9783319397566$q(electronic bk.)

Uncertaintythe soul of modeling, probability & statistics /
Briggs, William.

Uncertaintythe soul of modeling, probability & statistics /[electronic resource] :by William Briggs. - Cham :Springer International Publishing :2016. - xix, 258 p. :ill., digital ;24 cm.

Truth, Argument, Realism -- Logic -- Induction and Intellection -- What Probability Is -- What Probability Is Not -- Chance and Randomness -- Causality -- Probability Models -- Statistical and Physical Models -- Modelling Goals, Strategies, and Mistakes.

This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, such as out-of-the-box regression, cannot help in discovering cause. This new way of looking at uncertainty ties together disparate fields -- probability, physics, biology, the "soft" sciences, computer science -- because each aims at discovering cause (of effects) It broadens the understanding beyond frequentist and Bayesian methods to propose a Third Way of modeling. Presents a complete argument showing why probability should be treated as a part of logic Broadens understanding beyond frequentist and Bayesian methods, proposing a Third Way of modeling Proposes that p-values should die, and along with them, hypothesis testing William M. Briggs, PhD, is Adjunct Professor of Statistics at Cornell University. Having earned both his PhD in Statistics and MSc in Atmospheric Physics from Cornell University, he served as the editor of the American Meteorological Society journal and has published over 60 papers. He studies the philosophy of science, the use and misuses of uncertainty - from truth to modeling. Early in life, he began his career as a cryptologist for the Air Force, then slipped into weather and climate forecasting, and later matured into an epistemologist. Currently, he has a popular, long-running blog on the subjects written about here, with about 70,000 - 90,000 monthly readers.

ISBN: 9783319397566$q(electronic bk.)

Standard No.: 10.1007/978-3-319-39756-6doiSubjects--Topical Terms:

263216
Uncertainty
--Mathematical models.

LC Class. No.: QA273

Dewey Class. No.: 511.31

Uncertaintythe soul of modeling, probability & statistics /
LDR:03547nmm a2200325 a 4500 001 548969
003 DE-He213
005 20190724100036.0
006 m d
007 cr nn 008maaau
008 190822s2016 gw s 0 eng d
020 $a 9783319397566$q(electronic bk.)
020 $a 9783319397559$q(paper)
024 7 $a 10.1007/978-3-319-39756-6 $2 doi
035 $a 978-3-319-39756-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA273
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 511.31 $2 23
090 $a QA273 $b .B854 2016
100 1 $a Briggs, William. $3 828586
245 1 0 $a Uncertainty $h [electronic resource] : $b the soul of modeling, probability & statistics / $c by William Briggs.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xix, 258 p. : $b ill., digital ; $c 24 cm.
505 0 $a Truth, Argument, Realism -- Logic -- Induction and Intellection -- What Probability Is -- What Probability Is Not -- Chance and Randomness -- Causality -- Probability Models -- Statistical and Physical Models -- Modelling Goals, Strategies, and Mistakes.
520 $a This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, such as out-of-the-box regression, cannot help in discovering cause. This new way of looking at uncertainty ties together disparate fields -- probability, physics, biology, the "soft" sciences, computer science -- because each aims at discovering cause (of effects) It broadens the understanding beyond frequentist and Bayesian methods to propose a Third Way of modeling. Presents a complete argument showing why probability should be treated as a part of logic Broadens understanding beyond frequentist and Bayesian methods, proposing a Third Way of modeling Proposes that p-values should die, and along with them, hypothesis testing William M. Briggs, PhD, is Adjunct Professor of Statistics at Cornell University. Having earned both his PhD in Statistics and MSc in Atmospheric Physics from Cornell University, he served as the editor of the American Meteorological Society journal and has published over 60 papers. He studies the philosophy of science, the use and misuses of uncertainty - from truth to modeling. Early in life, he began his career as a cryptologist for the Air Force, then slipped into weather and climate forecasting, and later matured into an epistemologist. Currently, he has a popular, long-running blog on the subjects written about here, with about 70,000 - 90,000 monthly readers.
650 0 $a Uncertainty $x Mathematical models. $3 263216
650 1 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
650 2 4 $a Philosophy of Mathematics. $3 745264
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 348605
650 2 4 $a Epistemology. $3 275100
650 2 4 $a Logic. $3 180785
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-3-319-39756-6
950 $a Mathematics and Statistics (Springer-11649)