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Titre : Introduction to partial differential equations Type de document : Guide/Manuel Auteurs : David Borthwick, Auteur Editeur : Springer International Publishing Année de publication : 2016 Importance : 636 p. Format : 18 x 26 cm ISBN/ISSN/EAN : 978-3-319-48936-0 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Mathématique
[Termes IGN] équation de Laplace
[Termes IGN] équation différentielle
[Termes IGN] équation linéaire
[Termes IGN] onde acoustique
[Termes IGN] série de Fourier
[Termes IGN] transformation de FourierRésumé : (éditeur) This textbook is designed for a one year course covering the fundamentals of partial differential equations, geared towards advanced undergraduates and beginning graduate students in mathematics, science, engineering, and elsewhere. The exposition carefully balances solution techniques, mathematical rigor, and significant applications, all illustrated by numerous examples. Extensive exercise sets appear at the end of almost every subsection, and include straightforward computational problems to develop and reinforce new techniques and results, details on theoretical developments and proofs, challenging projects both computational and conceptual, and supplementary material that motivates the student to delve further into the subject.
No previous experience with the subject of partial differential equations or Fourier theory is assumed, the main prerequisites being undergraduate calculus, both one- and multi-variable, ordinary differential equations, and basic linear algebra. While the classical topics of separation of variables, Fourier analysis, boundary value problems, Green's functions, and special functions continue to form the core of an introductory course, the inclusion of nonlinear equations, shock wave dynamics, symmetry and similarity, the Maximum Principle, financial models, dispersion and solitons, Huygens'. Principle, quantum mechanical systems, and more make this text well attuned to recent developments and trends in this active field of contemporary research. Numerical approximation schemes are an important component of any introductory course, and the text covers the two most basic approaches: finite differences and finite elements.Note de contenu : 1- Introduction
2- Preliminaries
3- Conservation Equations and Characteristics
4- The Wave Equation
5- Separation of Variables
6- The Heat Equation
7- Function Spaces
8- Fourier Series
9- Maximum Principles
10- Weak Solutions
11- Variational Methods
12- Distributions
13- The Fourier TransformNuméro de notice : 25868 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel DOI : 10.1007/978-3-319-48936-0 En ligne : https://doi.org/10.1007/978-3-319-48936-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95528
Titre : Introduction to Time Series and Forecasting Type de document : Monographie Auteurs : Peter J. Brockwell, Auteur ; Richard A. Davis, Auteur Editeur : Springer International Publishing Année de publication : 2016 Importance : 425 p. Format : 21 x 28 cm ISBN/ISSN/EAN : 978-3-319-29854-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse spectrale
[Termes IGN] calcul matriciel
[Termes IGN] matrice de covariance
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modèle de simulation
[Termes IGN] modèle stochastique
[Termes IGN] série temporelle
[Termes IGN] variable aléatoireRésumé : (éditeur) This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Many additional special topics are also covered. Note de contenu : 1- Introduction
2- Stationary Processes
3- ARMA Models
4- Spectral Analysis
5- Modeling and Forecasting with ARMA Processes
6- Nonstationary and Seasonal Time Series Models
7- Time Series Models for Financial Data
8- Multivariate Time Series
9- State-Space Models
10- Forecasting Techniques
11- Further TopicsNuméro de notice : 25750 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie En ligne : https://link.springer.com/book/10.1007%2F978-3-319-29854-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94942 On estimation of the diagonal elements of a sparse precision matrix / Samuel Balmand in Electronic Journal of Statistics, vol 10 n° 1 (January 2016)
[article]
Titre : On estimation of the diagonal elements of a sparse precision matrix Type de document : Article/Communication Auteurs : Samuel Balmand , Auteur ; Arnak Dalalyan, Auteur Année de publication : 2016 Article en page(s) : pp 1551 - 1579 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] calcul matriciel
[Termes IGN] estimateur
[Termes IGN] matrice creuse
[Termes IGN] matrice de covariance
[Termes IGN] matrice diagonale
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] régression linéaire
[Termes IGN] résiduRésumé : (Auteur) In this paper, we present several estimators of the diagonal elements of the inverse of the covariance matrix, called precision matrix, of a sample of independent and identically distributed random vectors. The main focus is on the case of high dimensional vectors having a sparse precision matrix. It is now well understood that when the underlying distribution is Gaussian, the columns of the precision matrix can be estimated independently form one another by solving linear regression problems under sparsity constraints. This approach leads to a computationally efficient strategy for estimating the precision matrix that starts by estimating the regression vectors, then estimates the diagonal entries of the precision matrix and, in a final step, combines these estimators for getting estimators of the off-diagonal entries. While the step of estimating the regression vector has been intensively studied over the past decade, the problem of deriving statistically accurate estimators of the diagonal entries has received much less attention. The goal of the present paper is to fill this gap by presenting four estimators —that seem the most natural ones— of the diagonal entries of the precision matrix and then performing a comprehensive empirical evaluation of these estimators. The estimators under consideration are the residual variance, the relaxed maximum likelihood, the symmetry-enforced maximum likelihood and the penalized maximum likelihood. We show, both theoretically and empirically, that when the aforementioned regression vectors are estimated without error, the symmetry-enforced maximum likelihood estimator has the smallest estimation error. However, in a more realistic setting when the regression vector is estimated by a sparsity-favoring computationally efficient method, the qualities of the estimators become relatively comparable with a slight advantage for the residual variance estimator. Numéro de notice : A2016--107 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1214/16-EJS1148 Date de publication en ligne : 31/05/2016 En ligne : http://dx.doi.org/10.1214/16-EJS1148 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84707
in Electronic Journal of Statistics > vol 10 n° 1 (January 2016) . - pp 1551 - 1579[article]Documents numériques
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Titre : Probabilistic multi-person localisation and tracking Type de document : Thèse/HDR Auteurs : Tobias Klinger, Auteur ; Ingo Neumann, Directeur de thèse Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2016 Collection : DGK - C, ISSN 0065-5325 num. 787 Importance : 125 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-3-7696-5199-7 Note générale : bibliographie
PhD DissertationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de sensibilité
[Termes IGN] analyse multicritère
[Termes IGN] classification
[Termes IGN] détection de piéton
[Termes IGN] géolocalisation
[Termes IGN] image isolée
[Termes IGN] modèle stochastique
[Termes IGN] objet mobile
[Termes IGN] piéton
[Termes IGN] poursuite de cible
[Termes IGN] programmation linéaire
[Termes IGN] séquence d'images
[Termes IGN] similitude
[Termes IGN] surveillanceRésumé : (auteur) This dissertation investigates the problem of localising multiple persons in image sequences, while, at the same time, establishing temporal correspondences between single-frame locations. The aim of this work is the improvement of the reliability and precision of the generated trajectories, which is addressed by the formulation and investigation of a joint probabilistic model for the recursive filtering of the estimated positions. The trajectories are estimated in a common 3D object coordinate system, which was previously almost exclusively done in 2D. Note de contenu : 1. Introduction
1.1. Motivation
1.2. Research objectives and contributions
1.3. Outline of the dissertation
2. Basics
2.1. Probabilistic modelling
2.2. Recursive Bayesian estimation
2.3. Gaussian Process Regression
3. Related work
3.1. Tracking approaches
3.2. Observations
3.3. Temporal modelling
3.4. Data association
3.5. Discussion
4. A new probabilistic approach for multi-person localisation and tracking
4.1. Problem statement via Dynamic Bayesian Network
4.2. Observations
4.3. Temporal model
4.4. data association
4.5. Recursive estimation
4.6. Discussion
5. Experiments
5.1. Datasets and evaluation criteria
5.2. Sensitivity study and training
5.3. Model validation by ablation of its components
5.4. Multi-person localisation and tracking evaluation
6. Discussion of the results
6.1. Method evaluation
6.2. Evaluation of the trajectories
7. Conclusions and future workNuméro de notice : 19793 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Dissertation : : Stuttgart : 2016 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85037 Documents numériques
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Titre : Programming for computations, MATLAB/Octave Type de document : Guide/Manuel Auteurs : Svein Linge, Auteur ; Hans Petter Langtangen, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2016 Collection : Texts in Computational Science and Engineering, ISSN 1611-0994 num. 14 Importance : 216 p. ISBN/ISSN/EAN : 978-3-319-32452-4 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] analyse numérique
[Termes IGN] GNU Octave
[Termes IGN] MatlabRésumé : (Editeur) This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification. Numéro de notice : 22716 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Manuel informatique DOI : 10.1007/978-3-319-32452-4 En ligne : https://doi.org/10.1007/978-3-319-32452-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85228 PermalinkQGIS 2 cookbook / Alex Mandel (2016)PermalinkRemote Sensing Observations of Continental Surfaces, ch. 7. Digital Terrain Models derived from airborne lidar data / Clément Mallet (2016)PermalinkPermalinkPermalinkThe Minkowski approach for choosing the distance metric in geographically weighted regression / B. Lu in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)PermalinkTotal-variation-regularized low-rank matrix factorization for hyperspectral image restoration / Wei He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkTraitement du signal marégraphique par analyse harmonique pour la détermination d'une référence altimétrique pour l'Algérie / A. Rami in Bulletin des sciences géographiques, n° 30 (2015 - 2016)PermalinkA wildlife movement approach to optimally locate wildlife crossing structures / Rebecca W. Loraamm in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)PermalinkOptimal spatial land-use allocation for limited development ecological zones based on the geographic information system and a genetic ant colony algorithm / Nan Mi in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)Permalink