國立虎尾科技大學 |

Probabilistic numerics : = computation as machine learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Probabilistic numerics :/ Philipp Hennig, Michael A. Osborne, Hans P. Kersting.
其他題名:	computation as machine learning /
作者:	Hennig, Philipp,
其他作者:	Kersting, Hans P.,
面頁冊數:	1 online resource (xii, 398 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 10 Jun 2022).
標題:	Computer algorithms. -
電子資源:	https://doi.org/10.1017/9781316681411
ISBN:	9781316681411 (ebook)

Probabilistic numerics : = computation as machine learning /
Hennig, Philipp,

Probabilistic numerics :computation as machine learning /Philipp Hennig, Michael A. Osborne, Hans P. Kersting. - 1 online resource (xii, 398 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 10 Jun 2022).

Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.

ISBN: 9781316681411 (ebook)Subjects--Topical Terms:

528448
Computer algorithms.

LC Class. No.: Q325.5 / .H46 2022

Dewey Class. No.: 006.31

Probabilistic numerics : = computation as machine learning /
LDR:01946nam a2200277 i 4500 001 1122541
003 UkCbUP
005 20220620173323.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 240926s2022||||enk o ||1 0|eng|d
020 $a 9781316681411 (ebook)
020 $z 9781107163447 (hardback)
035 $a CR9781316681411
040 $a UkCbUP $b eng $e rda $c UkCbUP
050 4 $a Q325.5 $b .H46 2022
082 0 4 $a 006.31 $2 23
100 1 $a Hennig, Philipp, $e author. $3 1438818
245 1 0 $a Probabilistic numerics : $b computation as machine learning / $c Philipp Hennig, Michael A. Osborne, Hans P. Kersting.
264 1 $a Cambridge : $b Cambridge University Press, $c 2022.
300 $a 1 online resource (xii, 398 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Title from publisher's bibliographic system (viewed on 10 Jun 2022).
520 $a Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.
650 0 $a Computer algorithms. $3 528448
650 0 $a Machine learning $x Mathematics. $3 1340126
700 1 $a Kersting, Hans P., $e author. $3 1438820
700 1 $a Osborne, Michael A., $e author. $3 1438819
776 0 8 $i Print version: $z 9781107163447
856 4 0 $u https://doi.org/10.1017/9781316681411