• Study Resource
  • Explore
    • Arts & Humanities
    • Business
    • Engineering & Technology
    • Foreign Language
    • History
    • Math
    • Science
    • Social Science

    Top subcategories

    • Advanced Math
    • Algebra
    • Basic Math
    • Calculus
    • Geometry
    • Linear Algebra
    • Pre-Algebra
    • Pre-Calculus
    • Statistics And Probability
    • Trigonometry
    • other →

    Top subcategories

    • Astronomy
    • Astrophysics
    • Biology
    • Chemistry
    • Earth Science
    • Environmental Science
    • Health Science
    • Physics
    • other →

    Top subcategories

    • Anthropology
    • Law
    • Political Science
    • Psychology
    • Sociology
    • other →

    Top subcategories

    • Accounting
    • Economics
    • Finance
    • Management
    • other →

    Top subcategories

    • Aerospace Engineering
    • Bioengineering
    • Chemical Engineering
    • Civil Engineering
    • Computer Science
    • Electrical Engineering
    • Industrial Engineering
    • Mechanical Engineering
    • Web Design
    • other →

    Top subcategories

    • Architecture
    • Communications
    • English
    • Gender Studies
    • Music
    • Performing Arts
    • Philosophy
    • Religious Studies
    • Writing
    • other →

    Top subcategories

    • Ancient History
    • European History
    • US History
    • World History
    • other →

    Top subcategories

    • Croatian
    • Czech
    • Finnish
    • Greek
    • Hindi
    • Japanese
    • Korean
    • Persian
    • Swedish
    • Turkish
    • other →
 
Profile Documents Logout
Upload
Lecture 3
Lecture 3

Finite Markov Chains - classes.cs.uchicago.edu
Finite Markov Chains - classes.cs.uchicago.edu

... (a3) there exists a non-negative eigenvector to eigenvalue λ1 . ...
The exponential function for matrices
The exponential function for matrices

... to the linear system of differential equations X 0 = A X with initial condition X(0) = b, and it is given by exp(tA) b. Proof. We first verify that the function described above is indeed a solution by applying the Leibniz rule. If F (t) = exp(tA), the latter says that the derivative of the function ...
this document
this document

Statistical Behavior of the Eigenvalues of Random Matrices
Statistical Behavior of the Eigenvalues of Random Matrices

... and the corresponding eigenvalue is the value that would be measured if the system were in that state. (Because H is Hermitian, its eigenvalues are real.) In the case of an atomic nucleus, H is the “Hamiltonian”, and the eigenvalue En denotes the n-th energy level. Most nuclei have thousands of stat ...
8.1 and 8.2 - Shelton State
8.1 and 8.2 - Shelton State

Linear_Algebra.pdf
Linear_Algebra.pdf

powerpoint
powerpoint

... depend on time x = x(t). A partial derivative of f with respect to t is different from the full derivative because ...
Mathematics-XII - Kendriya Vidyalaya Ballygunge
Mathematics-XII - Kendriya Vidyalaya Ballygunge

Advanced Electrodynamics Exercise 5
Advanced Electrodynamics Exercise 5

Introduction Initializations A Matrix and Its Jordan Form
Introduction Initializations A Matrix and Its Jordan Form

multiply
multiply

... Modeling with Systems of Equations  There are 150 adults and 225 children at a zoo. If the zoo makes a total of $5100 from the entrance fees, and the cost of an adult and a child to attend is $31, how much does it cost for a parent or child to attend individually. Let a be the price of an adult ti ...
NECESSARY AND SUFFICIENT CONDITIONS FOR LTI SYSTEMS
NECESSARY AND SUFFICIENT CONDITIONS FOR LTI SYSTEMS

Theorems and counterexamples on structured
Theorems and counterexamples on structured

1 Model and Parameters. 2 Hilbert space in a Hubbard model.
1 Model and Parameters. 2 Hilbert space in a Hubbard model.

Linear models 2
Linear models 2

THE FUNDAMENTAL THEOREM OF ALGEBRA VIA LINEAR
THE FUNDAMENTAL THEOREM OF ALGEBRA VIA LINEAR

... where λ1 , λ2 ∈ R. Therefore AB = L1 (B) + iL2 (B) = (λ1 + iλ2 )B. Any nonzero column vector of B is an eigenvector in Cn for A. That concludes the proof of the Fundamental Theorem of Algebra for odd n, which was the base case. (Notice that the base case of a proof by induction is not always trivial ...
THE FUNDAMENTAL THEOREM OF ALGEBRA VIA LINEAR ALGEBRA
THE FUNDAMENTAL THEOREM OF ALGEBRA VIA LINEAR ALGEBRA

... where λ1 , λ2 ∈ R. Therefore AB = L1 (B) + iL2 (B) = (λ1 + iλ2 )B. Any nonzero column vector of B is an eigenvector in Cn for A. That concludes the proof of the Fundamental Theorem of Algebra for odd n, which was the base case. (Notice that the base case of a proof by induction is not always trivial ...
L - Calclab
L - Calclab

More on the Generalized Fibonacci Numbers and Associated
More on the Generalized Fibonacci Numbers and Associated

Definition
Definition

Chapter 3 Matrix Algebra with MATLAB
Chapter 3 Matrix Algebra with MATLAB

PRINCIPAL COMPONENT ANALYSIS
PRINCIPAL COMPONENT ANALYSIS

CURRICULUM SUMMARY – September to October 2008
CURRICULUM SUMMARY – September to October 2008

Beyond Vectors
Beyond Vectors

< 1 ... 62 63 64 65 66 67 68 69 70 ... 100 >

Perron–Frobenius theorem

In linear algebra, the Perron–Frobenius theorem, proved by Oskar Perron (1907) and Georg Frobenius (1912), asserts that a real square matrix with positive entries has a unique largest real eigenvalue and that the corresponding eigenvector can be chosen to have strictly positive components, and also asserts a similar statement for certain classes of nonnegative matrices. This theorem has important applications to probability theory (ergodicity of Markov chains); to the theory of dynamical systems (subshifts of finite type); to economics (Okishio's theorem, Leontief's input-output model); to demography (Leslie population age distribution model), to Internet search engines and even ranking of football teams.
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report