2 edition of **Symbolic computation for statistics using tensors** found in the catalog.

Symbolic computation for statistics using tensors

Wang, Yong.

- 304 Want to read
- 20 Currently reading

Published
**1992** by [s.n.] in Toronto .

Written in English

**Edition Notes**

Thesis (Ph.D.)--University of Toronto, 1992.

Statement | Yong Wang. |

ID Numbers | |
---|---|

Open Library | OL18740212M |

A precise, self-contained treatment of Galois theory, this Dover Aurora original features detailed proofs and complete solutions to exercises. The approach advances from introductory material to extensions that contribute to a comprehensive understanding of the Galois group of a polynomial. Final chapters offer excellent discussions of several real-world applications. edition. Wolfram Community forum discussion about [Code-Sharing] Defining symbolic Reals, Tensors, Vectors and Scalars. Stay on top of important topics and build connections by joining Wolfram Community groups relevant to your interests. Symbolic Statistics - input and output with mathematical notation. Support for exact and arbitrary precision computations. Define custom statistical models or use any of 38 predefined probability distributions. Quickly generate large samples according to these distributions. Apply standard statistical functions to data or statistical distributions. Get this from a library! Mathematics for physical science and engineering: symbolic computing applications in Maple and Mathematica. [Frank E Harris] -- This is a complete text in mathematics for physical science that includes the use of symbolic computation to illustrate the mathematical concepts and enable the solution of a broader range of.

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Mathematical symbol. Tensor analysis, it is claimed despite all evidence to the contrary, has nothing whatever to do with indices. ‘Coordinate-free methods’ and ‘operator calculus’ are but two of the rallying slogans for mathematicians of this persuasion.

‘Computation’, on the other hand, is a reactionary and subversive Size: 1MB. Tensor formulas are written in the form close to that of classical textbooks in GRG, with the only difference that the summation symbol appears explicitly. Tensors are functions, not matrixes, and their components are evaluated lazily.

This means that only these components which are indispensable to realize the final task are computed. Symbolic Tensors. Tensors are fundamental tools for linear computations, generalizing vectors and matrices to higher ranks. The Wolfram Language includes powerful methods to algebraically manipulate tensors with any rank and symmetry.

It handles both tensors given as arrays of components and symbolic tensors given as members of specific tensor domains. A method is presented for the computer calculation of curvature tensors in symbolic form.

First, the difficulties in calculating the Riemann tensor R ναβ μ, the contracted quantities R νβ, and R using the FORMAC languange and the IBM computer are reported, and it is suggested that a compact method of calculation using differential forms would be helpful in terms of reducing problem Cited by: 4.

While Mathematica is surely capable of handling abstract tensors/differential geometry computation, not so much capability is already built in but for special cases. Very recent versions have added tensor capabilities and the ability to define arbitrary coordinate systems, you may want to give it a try but I have no experience with it.

A new eigenvalue localization set for tensors is given, and proved to be tighter than those in [L. Qi, Eigenvalues of a real supersymmetric tensor. Journal of Symbolic Computation 40 () Built-in Symbolic Tensors.

Mathematica 9 introduces support for symbolic array objects, from simple vectors to arrays of any rank, dimensions, and symmetry. New tensor algebra operations allow the construction of polynomials of symbolic arrays.

Mathematics for Physical Science and Engineering is a complete text in mathematics for physical science that includes the use of symbolic computation to illustrate the mathematical concepts and enable the solution of a broader range of practical problems. This book enables professionals to connect their knowledge of mathematics to either or both of the symbolic languages Maple and Mathematica.

Theory and Computation of Tensors: Multi-Dimensional Arrays investigates theories and computations of tensors to broaden perspectives on matrices. Data in the Big Data Era is not only. The symbolic computation of integrability operator is a computationally hard problem and the book covers a huge number of situations through tutorials.

The mathematical part of the book is a new approach to integrability structures that allows to treat all of them in a unified way. The software is an official package of Reduce. SIAM Journal on Matrix Analysis and ApplicationsOn determinants and eigenvalue theory of tensors.

Journal of Symbolic Computat () Nonnegative non-redundant tensor decomposition. On the Tensor SVD and the Optimal Low Rank Orthogonal Approximation of Tensors. SIAM Journal on Matrix Analysis and Applications Cited by: An Introduction To Tensors for Students of Physics and Engineering Joseph C.

Kolecki National Aeronautics and Space Administration Glenn Research Center Cleveland, Ohio Tensor analysis is the type of subject that can make even the best of students shudder.

My ownFile Size: KB. Layer called with an input that isn't a symbolic tensor keras. I'm trying to pass the output of one layer into two different layers and then join them back together.

Theory and Computation of Tensors: Multi-Dimensional Arrays - Kindle edition by Wei, Yimin, Ding, Weiyang. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Theory and Computation of Tensors: Multi-Dimensional : $ Large scale tensor analysis by computer.

The paper contains a description of the software package MathTensor which can be loaded into the Mathematica computer algebra system. The package is useful for manipulating large systems of equations and for detecting symmetries in tensor structures. The author addresses problems emerging from quantum field theory of curved space-times for instance to.

Mathematica programming is also introduced with accompanying examples. In the second half of the book, an analysis of heterogeneous materials with emphasis on composites is covered.

Takes a practical approach by using Mathematica, one of the most popular programmes for symbolic computation. It allows working with symbolic matrices and symbolic block matrices (e.g.

symbolic block matrix inversion). xAct - a package designed by researchers for large scale projects in general relativity; subpackages capable of extensive tensor manipulation (xTensor, xCoba) as well as perturbation theory in general relativity to any order (xPert). I really, really love Manifolds, Tensors, and Forms: An Introduction for Mathematicians and Physicists by Paul Renteln.

It is mathematical—sorry—but it gives the bare-bones definitions that are needed to do differential geometry. So all of the ele. the Representation of Symbolic Structures in Connectionist Systems Paul Smolensky Department of Computer Science and Institute of Cognitive Science, University of Colorado, Boulder, COUSA ABSTRACT A general method, the tensor product representation, is defined for Cited by: Mathematics for Physical Science and Engineering is a complete text in mathematics for physical science that includes the use of symbolic computation to illustrate the mathematical concepts and enable the solution of a broader range of practical problems.

This book enables professionals to connect their knowledge of mathematics to either or both of the symbolic languages Maple and by: 4.

Furthermore, we developed an algorithm for symbolic computation of the Moore-Penrose inverse of a polynomial matrix using the full-rank QDR decomposition, therefore maximizing the potential of. A general symbolic tensor expression can be understood as a linear combination of terms formed by combining the symbolic tensors using three basic operations: tensor products, transpositions, and contractions.

Other basic algebra operations can be decomposed in terms of these. Maxima is a symbolic computation platform that is free, open source, runs on Windows, Linux, and Mac, and covers a wide range of mathematical functions, including 2-D/3-D plotting and animation. Capabilities include algebraic simplification, polynomials, methods from calculus, matrix equations, differential equations, number theory, combinatorics, hypergeometric functions, tensors.

Computation graph is the essential concept of symbolic computation where the tensors in this case define the steps of the computation and the graph compilation (achieved by on API) turns the graph into a function. Note that before compiling, the elements in the graph are merely symbols.

Therefore a linear map between matrices is also a tensor). Tensors are inherently related to vector spaces and their dual spaces, and can take several different forms – for example: a scalar, a tangent vector at a point, a cotangent vector (dual vector) at a point, or a multi-linear map between vector spaces.

SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible.

SymPy is written entirely in Python. Free: Licensed under BSD, SymPy is free both as in speech and as in beer. Multihomogeneous polynomial decomposition using moment matrices. In: Leykin, A. (Ed.), International Symposium on Symbolic and Algebraic Computation (ISSAC), ACM, New York.

Google Scholar Digital Library; Bernardi et al, b. Computing symmetric rank for symmetric tensors. Symbolic Comput. v Google Scholar Digital Library. Mainframe Computer algebra - CA, is a branch of Symbolic computation (or symbolic mathematics - SM) Symbolic mathematics relates to the use of computers to manipulate mathematical equations and expressions in symbolic form, as opposed to manipulating the approximations of specific numerical quantities represented by those symbols.

Journal of Symbolic Computat Carlos Beltrán, Paul Breiding, and Nick Vannieuwenhoven. () Pencil-Based Algorithms for Tensor Rank Decomposition are not by: 1 The index notation Before we start with the main topic of this booklet, tensors, we will ﬁrst introduce a new notation for vectors and matrices, and their algebraic manipulations: the indexFile Size: KB.

Book Description Oxford Elsevier LTD JunBuch. Condition: Neu. Neuware - Mathematics for Physical Science and Engineering is a complete text in mathematics for physical science that includes the use of symbolic computation to illustrate the mathematical concepts and enable the solution of a broader range of practical Range: $ - $ The main functionalities of a CAS are to perform numerical computations, symbolic computations, data analysis, and data visualization.

Due to its widespread domain, various computer algebra systems for Linux exist, dealing with different types of applications. Theory and Computation of Tensors: Multi-Dimensional Arrays investigates theories and computations of tensors to broaden perspectives on matrices.

Data in the Big Data Era is not only growing larger but also becoming much more complicated. Tensors (multi-dimensional arrays) arise naturally from many engineering or scientific disciplines because they can represent multi-relational.

Symbolic computation is handling non-numerical values, this means symbols like in algebra. There is a powerful free symbolic computation program for multiple platforms, maxima, that lets you, e.g., simplify or expand arithmetic expressions with symbols, of integrate or differentiate them, among others.

Just try it out - it is fun and useful. Symbol Tensor is Protected. Symbol TensorType is Protected. Symbol TensorName is Protected.

is because you loaded TensoriaCalc more than once in the same kernel session. When writing the package, I had to Protect all the symbols used in the package, such as Tensor, Metric, etc. This means their definitions cannot be altered by an external user. Books shelved as tensor-analysis: Elasticity: Tensor, Dyadic, and Engineering Approaches by Pei Chi Chou, Vector and Tensor Analysis with Applications by.

In computational mathematics, computer algebra, also called symbolic computation or algebraic computation, is a scientific area that refers to the study and development of algorithms and software for manipulating mathematical expressions and other mathematical objects. As an application, we use this method to study powers of the construction given by Coppersmith and Winograd [Journal of Symbolic Computation, ] and obtain the upper bound ω.

One final note is that the Wolfram Language cannot do symbolic matrix calculus when the number of dimensions aren't specified, which makes working with multivariate distributions somewhat of a chore. For example, the same calculation for a multivariate normal specified using matrix algebra doesn't work, since the Wolfram Language can't.

Tensors touch upon many areas in mathematics and computer science. Though classical, the study of tensors has recently gained fresh momentum due to applications in such areas as complexity theory and algebraic statistics.

Many concrete questions in the field remain open, and computational methods help expand the boundaries of our current understanding and drive progress in the. Mathematics for Physical Science and Engineering is a complete text in mathematics for physical science that includes the use of symbolic computation to illustrate the mathematical concepts and enable the solution of a broader range of practical problems.Get this from a library!

Symbolic computation for statistical inference. [D F Andrews; J E H Stafford] -- "This book summarizes a decade of research into the use of symbolic computation applied to statistical inference problems. It shows the considerable potential of the subject to automate statistical.L.

Qi / Journal of Symbolic Computation 40 () – for all tensor product (Qi and Teo, ), Axm−1 for a vector x ∈ Rn denotes a vector in Rn,whose ith component is n i2,im=1 Ai,i2,im xi2 xim. Qi () called a real number λ an H-eigenvalueof A if it and a nonzero real vector x are solutions of the following homogeneous polynomial equation.