國立虎尾科技大學 |

Beyond Traditional Probabilistic Methods in Economics

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Beyond Traditional Probabilistic Methods in Economics/ edited by Vladik Kreinovich, Nguyen Ngoc Thach, Nguyen Duc Trung, Dang Van Thanh.
其他作者:	Kreinovich, Vladik.
面頁冊數:	XIV, 1157 p. 206 illus., 124 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-04200-4
ISBN:	9783030042004

Beyond Traditional Probabilistic Methods in Economics
Beyond Traditional Probabilistic Methods in Economics[electronic resource] /edited by Vladik Kreinovich, Nguyen Ngoc Thach, Nguyen Duc Trung, Dang Van Thanh. - 1st ed. 2019. - XIV, 1157 p. 206 illus., 124 illus. in color.online resource. - Studies in Computational Intelligence,8091860-949X ;. - Studies in Computational Intelligence,564.

This book presents recent research on probabilistic methods in economics, from machine learning to statistical analysis. Economics is a very important – and at the same a very difficult discipline. It is not easy to predict how an economy will evolve or to identify the measures needed to make an economy prosper. One of the main reasons for this is the high level of uncertainty: different difficult-to-predict events can influence the future economic behavior. To make good predictions and reasonable recommendations, this uncertainty has to be taken into account. In the past, most related research results were based on using traditional techniques from probability and statistics, such as p-value-based hypothesis testing. These techniques led to numerous successful applications, but in the last decades, several examples have emerged showing that these techniques often lead to unreliable and inaccurate predictions. It is therefore necessary to come up with new techniques for processing the corresponding uncertainty that go beyond the traditional probabilistic techniques. This book focuses on such techniques, their economic applications and the remaining challenges, presenting both related theoretical developments and their practical applications.

ISBN: 9783030042004

Standard No.: 10.1007/978-3-030-04200-4doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Beyond Traditional Probabilistic Methods in Economics
LDR:02715nam a22003855i 4500 001 1008496
003 DE-He213
005 20200630144918.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030042004 $9 978-3-030-04200-4
024 7 $a 10.1007/978-3-030-04200-4 $2 doi
035 $a 978-3-030-04200-4
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Beyond Traditional Probabilistic Methods in Economics $h [electronic resource] / $c edited by Vladik Kreinovich, Nguyen Ngoc Thach, Nguyen Duc Trung, Dang Van Thanh.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XIV, 1157 p. 206 illus., 124 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 Studies in Computational Intelligence, $x 1860-949X ; $v 809
520 $a This book presents recent research on probabilistic methods in economics, from machine learning to statistical analysis. Economics is a very important – and at the same a very difficult discipline. It is not easy to predict how an economy will evolve or to identify the measures needed to make an economy prosper. One of the main reasons for this is the high level of uncertainty: different difficult-to-predict events can influence the future economic behavior. To make good predictions and reasonable recommendations, this uncertainty has to be taken into account. In the past, most related research results were based on using traditional techniques from probability and statistics, such as p-value-based hypothesis testing. These techniques led to numerous successful applications, but in the last decades, several examples have emerged showing that these techniques often lead to unreliable and inaccurate predictions. It is therefore necessary to come up with new techniques for processing the corresponding uncertainty that go beyond the traditional probabilistic techniques. This book focuses on such techniques, their economic applications and the remaining challenges, presenting both related theoretical developments and their practical applications.
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 0 $a Economic theory. $3 809881
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Economic Theory/Quantitative Economics/Mathematical Methods. $3 1069071
700 1 $a Kreinovich, Vladik. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 964800
700 1 $a Thach, Nguyen Ngoc. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1279446
700 1 $a Trung, Nguyen Duc. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1302320
700 1 $a Van Thanh, Dang. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1302321
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030041991
776 0 8 $i Printed edition: $z 9783030042011
830 0 $a Studies in Computational Intelligence, $x 1860-949X ; $v 564 $3 1253640
856 4 0 $u https://doi.org/10.1007/978-3-030-04200-4
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)