Home  - Pure_And_Applied_Math - Approximations Expansions

Books Baby Camera Phones Computers Games DVD Electronics Kitchen Magazines Music Garden Software Tools Toys Video Apparel & Accessories Jewelry & Watches Musical Instruments Health & Personal Care Beauty Sports & Outdoors Office Products

e99.com Bookstore

Images

Newsgroups

61-71 of 71    Back | 1  | 2  | 3  | 4

Approximations Expansions:     more books (94)

Discrete Hamiltonian Systems: Difference Equations, Continued Fractions, and Riccati Equations (Texts in the Mathematical Sciences) by Calvin Ahlbrandt, A.C. Peterson, 2010-11-02 Parametric Continuation and Optimal Parametrization in Applied Mathematics and Mechanics by V.I. Shalashilin, E. B. Kuznetsov, 2010-11-02 Number Theory: Tradition and Modernization (Developments in Mathematics) Geometric Properties for Incomplete Data (Computational Imaging and Vision) Recent Progress in Inequalities (Mathematics and Its Applications) Non-Connected Convexities and Applications (Applied Optimization) by G. Cristescu, L. Lupsa, 2002-05-31 Wavelets in Signal and Image Analysis: From Theory to Practice (Computational Imaging and Vision) Variational Theory of Splines by Anatoly Yu. Bezhaev, Vladimir A. Vasilenko, 2010-11-02 Summability of Multi-Dimensional Fourier Series and Hardy Spaces (Mathematics and Its Applications) by Ferenc Weisz, 2002-03-31 Developments and Applications of Block Toeplitz Iterative Solvers (Combinatorics and Computer Science) by Xiao-Qing Jin, 2010-11-02 Orthogonal Polynomials and Special Functions: Computation and Applications (Lecture Notes in Mathematics) (Volume 0) Walsh Equiconvergence of Complex Interpolating Polynomials (Springer Monographs in Mathematics) by Amnon Jakimovski, Ambikeshwar Sharma, et all 2010-11-02 Explorations in Harmonic Analysis: With Applications to Complex Function Theory and the Heisenberg Group (Applied and Numerical Harmonic Analysis) by Steven G. Krantz, 2009-05-05 Theory and Applications of Special Functions: A Volume Dedicated to Mizan Rahman (Developments in Mathematics)

lists with details

61. The Use Of Padé Approximation To Eliminate Nonuniformities Of
    Your browser may not have a PDF reader available. Google recommends visiting our text version of this document.  
    http://www.springerlink.com/index/M53H327601306477.pdf

62. Wiley InterScience :: Session Cookies
    Your browser may not have a PDF reader available. Google recommends visiting our text version of this document.  
    http://doi.wiley.com/10.1002/qua.560440829
    
    Extractions: @import "/freeflow/css/wis.standards.css"; Skip to Content If you are seeing this message, you may be experiencing temporary network problems. Please wait a few minutes and refresh the page. If the problem persists, you may wish to report it to your local Network Manager. It is also possible that your web browser is not configured or not able to display style sheets. In this case, although the visual presentation will be degraded, the site should continue to be functional. We recommend using the latest version of Microsoft or Mozilla web browser to help minimise these problems. An error has occured because we were unable to send a cookie to your web browser. Session cookies are commonly used to facilitate improved site navigation. In order to use Wiley InterScience you must have your browser set to accept cookies. Once you have logged in to Wiley InterScience, our Web server uses a temporary cookie to help us manage your visit. This Session Cookie is deleted when you logoff Wiley InterScience, or when you quit your browser. The cookie allows us to quickly determine your access control rights and your personal preferences during your online session. The Session Cookie is set out of necessity and not out of convenience. About Wiley InterScience About Wiley Privacy

63. First Steps In Numerical Analysis
    Note that this expansion has only oddpowered terms so, although the polynomial approximation is of degree (2k - 1 ), it has only k terms.  
    http://mpec.sc.mahidol.ac.th/numer/STEP5.HTM
    
    Extractions: An important procedure in Analysis is to represent a given function as an infinite series of terms involving simpler or otherwise more appropriate functions. Thus, if f is the given function, it may be represented as the series expansion involving the set of functions . Mathematicians have spent a lot of effort in discussing the convergence of series, i.e., on defining conditions for which the partial sum approximates the function value f (x) ever more closely as n increases. In Numerical Analysis, we are primarily concerned with such convergent series; computation of the sequence of partial sums is an approximation process in which the truncation error may be made as small as we please by taking sufficient terms into account. The most important expansion to represent a function is the Taylor series. If f is suitably smooth in the neighbourhood of some chosen point x we have where h = x - x denotes the displacement from x to the point x in the neighbourhood, and the rcmainder term is

64. Chicago Journals - The Astrophysical Journal
    Indeed, it is as important that a good approximation can be obtained when the expansion is truncated after a small number of terms as it is for the  
    http://www.journals.uchicago.edu/cgi-bin/resolve?1997ApJ...483..464L

65. Approximation Inference (SNN Nijmegen)
    This approximation, which has its origin in statistical physics, can be viewed as a first order expansion around the ultimate tractable distribution the  
    http://www.snn.ru.nl/nijmegen/approx-graph.php3
    
    Extractions: Funding: STW Pionier. The aims of this project are to develop novel approximation methods for learning and inference in complex probability models. Inference is the problem to compute marginal probabilities given a probability model. Learning is the problem to estimate model parameters from data. The learning problem contains in general the inference problem as a subtask. A major problem in probabilistic modelling with many variables is the computational complexity involved in typical calculations for inference. For sparsely connected probabilistic networks this problem has been solved during the last decades by the invention of efficient algorithms for exact inference. However, in large, densely connected models exact inference is intractable . This means that the computation time increases exponentially with the problem size. In such a case, sampling methods, like Markov Chain Monte Carlo (MCMC) may seem a straightforward solution, but may require extreme long computation time to gather a sufficient amount of samples. An alternative solution is provided by variational methods. These methods do the approximations directly by fitting distributions rather than by gathering statistics from samples. Basically, variational methods expand the intractable distribution around an approximate distribution that is tractable. One of the simplest and most prominent variational approximations is the so-called mean field approximation. This approximation, which has its origin in statistical physics, can be viewed as a first order expansion around the ultimate tractable distribution: the one in which all variables are decoupled.

66. Komatsu: On Inhomogeneous Diophantine Approximation With Some Quasi-periodic Exp
    5 T. Komatsu On inhomogeneous continued fraction expansion and inhomogeneous Diophantine approximation. J. Number Theory 62 (1997), 192212.  
    http://jtnb.cedram.org/jtnb-bin/fitem?id=JTNB_1999__11_2_331_0

67. Neural Comp. -- Sign In Page
    Using perturbation expansion of the Kullback divergence (or Plefka expansion in statistical physics), a formulation of meanfield approximation of general  
    http://neco.mitpress.org/cgi/content/full/12/8/1951
    
    Extractions: Password Forgot your user name or password? Sign Up Subscribe to the Journal - Subscribe to the print and/or online journal. This Article Abstract Full Text (PDF) Alert me when this article is cited ... Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Tanaka, T. Search for Related Content PubMed PubMed Citation Articles by Tanaka, T. HOME HELP ... J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS

68. Taylor Series
    derivative approximation. gradient approximation. gradientHessian approximation. Taylor polynomial. Taylor series expansion  
    http://riskglossary.com/articles/taylor_series.htm
    
    Extractions: Consider a function f that is differentiable in an open interval about some point The linear polynomial is called a derivative approximation . It provides a good approximation for f , at least in a small interval about . This is because: equals f at , and has the same first derivative as f at If f is twice differentiable in an open interval about , we can improve the approximation with a quadratic polynomial Consider the function which has first and second derivatives on . Let’s construct a linear polynomial approximation for f about the point = 0. Applying [

69. Faculty_profile
    Dielectric response of solid state plasmas; random phase approximation; of periodic lattice in the harmonic approximation, eigenvector expansion of  
    http://www.stevens.edu/ses/about_soe/faculty/faculty_profile.php?faculty_id=437

70. CJO - Abstract - Estimates Of The Errors Incurred In Various Asymptotic Represen
    Your browser may not have a PDF reader available. Google recommends visiting our text version of this document.  
    http://journals.cambridge.org/production/action/cjoGetFulltext?fulltextid=389738

71. AN EXPANSION FORMULA FOR THE DRAG ON A CIRCULAR CYLINDER MOVING
    Your browser may not have a PDF reader available. Google recommends visiting our text version of this document.  
    http://qjmam.oxfordjournals.org/cgi/reprint/4/4/401.pdf

61-71 of 71    Back | 1  | 2  | 3  | 4