Muutke küpsiste eelistusi

E-raamat: From Statistical Physics to Data-Driven Modelling: with Applications to Quantitative Biology

4.00/5 (2 hinnangut Goodreads-ist)
(Director of Research, CNRS, École Normale Supérieure), (Researcher, CNRS, École Normale Supérieure), (Director of Research, CNRS, École Normale Supérieure)
  • Formaat: 192 pages
  • Ilmumisaeg: 26-Sep-2022
  • Kirjastus: Oxford University Press
  • Keel: eng
  • ISBN-13: 9780192633729
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 48,15 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
From Statistical Physics to Data-Driven Modelling: with Applications to Quantitative BiologySuurem pilt
  • Formaat: 192 pages
  • Ilmumisaeg: 26-Sep-2022
  • Kirjastus: Oxford University Press
  • Keel: eng
  • ISBN-13: 9780192633729
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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.

Arvustused

This book addresses crucially important questions and delivers a unique outlook on a timely topic. * Guido Caldarelli, Ca' Foscari University of Venice * Modern post-genome biology and medicine are in the middle of a quantitative revolution and this unique and timely book by three experienced researchers will be indispensable to anyone studying or interested in the topic. * A.C.C. Coolen, Radboud University, Nijmegen * This is a much-needed text on an extremely relevant topic, written by three authors with considerable experience and expertise. * Massimo Vergassola, École Normale Supérieure, Paris *

1 Introduction to Bayesian inference
1(16)
1.1 Why Bayesian inference?
1(1)
1.2 Notations and definitions
2(2)
1.3 The German tank problem
4(3)
1.4 Laplace's birth rate problem
7(4)
1.5 Tutorial 1: diffusion coefficient from single-particle tracking
11(6)
2 Asymptotic inference and information
17(22)
2.1 Asymptotic inference
17(6)
2.2 Notions of information
23(6)
2.3 Inference and information: the maximum entropy principle
29(3)
2.4 Tutorial 2: entropy and information in neural spike trains
32(7)
3 High-dimensional inference: searching for principal components
39(20)
3.1 Dimensional reduction and principal component analysis
39(4)
3.2 The retarded learning phase transition
43(9)
3.3 Tutorial 3: replay of neural activity during sleep following task learning
52(7)
4 Priors, regularisation, sparsity
59(22)
4.1 Lp-norm based priors
59(5)
4.2 Conjugate priors
64(3)
4.3 Invariant priors
67(4)
4.4 Tutorial 4: sparse estimation techniques for RNA alternative splicing
71(10)
5 Graphical models: from network reconstruction to Boltzmann machines
81(26)
5.1 Network reconstruction for multivariate Gaussian variables
81(5)
5.2 Boltzmann machines
86(6)
5.3 Pseudo-likelihood methods
92(5)
5.4 Tutorial 5: inference of protein structure from sequence data
97(10)
6 Unsupervised learning: from representations to generative models
107(30)
6.1 Autoencoders
107(5)
6.2 Restricted Boltzmann machines and representations
112(8)
6.3 Generative models
120(5)
6.4 Learning from streaming data: principal component analysis revisited
125(7)
6.5 Tutorial 6: online sparse principal component analysis of neural assemblies
132(5)
7 Supervised learning: classification with neural networks
137(24)
7.1 The perceptron, a linear classifier
137(6)
7.2 Case of few data: overfitting
143(3)
7.3 Case of many data: generalisation
146(6)
7.4 A glimpse at multi-layered networks
152(4)
7.5 Tutorial 7: prediction of binding between PDZ proteins and peptides
156(5)
8 Time series: from Markov models to hidden Markov models
161(14)
8.1 Markov processes and inference
161(3)
8.2 Hidden Markov models
164(7)
8.3 Tutorial 8: CG content variations in viral genomes
171(4)
References 175(6)
Index 181
Simona Cocco is a research Director at the Ecole Normale Supérieure in Paris, working on statistical physics, biophysics, and inference of models from data. In 2000, she received a double PhD in Physics from the Ecole Normale Supérieure in Lyon and Biophysics from the University of Rome Sapienza and was then a postdoc at the ENS in Paris and in Chicago, before joining the CNRS in 2001 as a permanent researcher. Between 2009 and 2011 she was a senior member at the Institute of Advanced Study in Princeton.



Rémi Monasson is a research Director at the CNRS and the Ecole Normale Supérieure, and a professor at the Ecole Polytechnique. He did his PhD on the statistical mechanics of neural networks, and was then a postdoc in Rome, working on disordered systems and phase transitions in optimisation problems. He later worked on biophysics and systems biology in Chicago and at the Institute for Advanced Study in Princeton. His research interests lie at the intersection of statistical physics, machine learning and computational biology.



Francesco Zamponi received his PhD in Theoretical Physics from the University of Rome Sapienza and was then a postdoc at the ENS and the CEA in Paris, before joining the CNRS in 2008 as a permanent researcher. He is currently based at the Physics Department of the ENS in Paris. His research is driven by the application of ideas and methods issued from the statistical mechanics of complex systems, to problems arising in classical and quantum condensed matter, biology, information theory, and mathematics. He has published over 130 research articles, and is the author of a chapter for the Handbook of Satisfiability (IOS Press 2021) and a book on the Theory of Simple Glasses (Cambridge University Press 2019). He was awarded an ERC Consolidator grant (GlassUniversality) and is one of the Principal Investigators of the Simons collaboration on Cracking the glass problem.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97801926337292e.html
Märksõnad: