000			02028nam a22002657a 4500
005			20250627120748.0
008			250627b |||||||| |||| 00| 0 eng d
020			_a9780198864745
040			_aCSL _cCSL
041			_2eng _aeng
084			_aCN2 R2 _qCSL
100			_aCocco, Simona _eauthor. _9814805
245			_aFrom Statistical Physics to Data-Driven Modelling _b: with Applications to Quantitative Biology
260			_aOxford : _bOxford University Press, _c2022.
300			_avi, 183p. _b: ill. _c; 25 cm.
500			_aIncludes references and index.
520			_aThe 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			_aStatistical physics. _9713592
650			_aBayesian inference. _9814806
650			_aGraphical models. _9734433
650			_aMarkov models. _9448668
700			_aMonasson, Rémi _eco-author. _9814807
700			_aZamponi, Francesco _eco-author. _9814808
942			_2CC _cTEXL _hCN2 R2 _n0
999			_c1433060 _d1433060