From Statistical Physics to Data-Driven Modelling (Record no. 1433060)

MARC details
000 -LEADER
fixed length control field 02028nam a22002657a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250627120748.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250627b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780198864745
040 ## - CATALOGING SOURCE
Original cataloging agency CSL
Transcribing agency CSL
041 ## - LANGUAGE CODE
Source of code eng
Language code of text/sound track or separate title eng
084 ## - COLON CLASSIFICATION NUMBER
Classification number CN2 R2
Assigning agency CSL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Cocco, Simona
Relator term author.
9 (RLIN) 814805
245 ## - TITLE STATEMENT
Title From Statistical Physics to Data-Driven Modelling
Remainder of title : with Applications to Quantitative Biology
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Oxford :
Name of publisher, distributor, etc. Oxford University Press,
Date of publication, distribution, etc. 2022.
300 ## - PHYSICAL DESCRIPTION
Extent vi, 183p.
Other physical details : ill.
Dimensions ; 25 cm.
500 ## - GENERAL NOTE
General note Includes references and index.
520 ## - SUMMARY, ETC.
Summary, etc. The study of most scientific fields now relies on an ever-increasing amount of data, due to instrumental and experimental progress in monitoring and manipulating complex systems made of many microscopic constituents. How can we make sense of such data, and use them to enhance our understanding of biological, physical, and chemical systems.Aimed at graduate students in physics, applied mathematics, and computational biology, the primary objective of this textbook is to introduce the concepts and methods necessary to answer this question at the intersection of probability theory, statistics, optimisation, statistical physics, inference, and machine learning.The second objective of this book is to provide practical applications for these methods, which will allow students to assimilate the underlying ideas and techniques. While readers of this textbook will need basic knowledge in programming (Python or an equivalent language), the main emphasis is not on mathematical rigour, but on the development of intuition and the deep connections with statistical physics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Statistical physics.
9 (RLIN) 713592
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Bayesian inference.
9 (RLIN) 814806
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Graphical models.
9 (RLIN) 734433
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Markov models.
9 (RLIN) 448668
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Monasson, Rémi
Relator term co-author.
9 (RLIN) 814807
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Zamponi, Francesco
Relator term co-author.
9 (RLIN) 814808
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Colon Classification (CC)
Koha item type Textual
Classification part CN2 R2
Suppress in OPAC No
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Colon Classification (CC)     Central Science Library Central Science Library 2024-10-28 Prashant Book Agency   CN2 R2 SL1655988 2025-06-27 2025-06-27 Textual
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