Eigenvalue and Eigenvector, Determinants, Principal Components von Edu Pristine

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Über den Vortrag

Der Vortrag „Eigenvalue and Eigenvector, Determinants, Principal Components“ von Edu Pristine ist Bestandteil des Kurses „ARCHIV Linear Mathematics and Matrix Algebra“. Der Vortrag ist dabei in folgende Kapitel unterteilt:

  • Positive Defiteness
  • Cholesky Decomposition
  • Eigenvalues and Eigenvector
  • Determinants
  • Principal Components
  • Questions and Answers

Dozent des Vortrages Eigenvalue and Eigenvector, Determinants, Principal Components

 Edu Pristine

Edu Pristine

Trusted by Fortune 500 Companies and 10,000 Students from 40+ countries across the globe, EduPristine is one of the leading International Training providers for Finance Certifications like FRM®, CFA®, PRM®, Business Analytics, HR Analytics, Financial Modeling, Operational Risk Modeling etc. It was founded by industry professionals who have worked in the area of investment banking and private equity in organizations such as Goldman Sachs, Crisil - A Standard & Poors Company, Standard Chartered and Accenture.

EduPristine has conducted corporate training for various leading corporations and colleges like JP Morgan, Bank of America, Ernst & Young, Accenture, HSBC, IIM C, NUS Singapore etc. EduPristine has conducted more than 500,000 man-hours of quality training in finance.
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Auszüge aus dem Begleitmaterial

... Portfolio Return and risk "Positive definiteness" Eigen value ...

... Knowledge Management Pristine2 "Definitions An square real symmetric matrix M is positive definite if zT MZ > 0 for all non-zero vectors z with real entries (z R) An square real symmetric matrix M is negative definite if zT MZ < 0 for all non-zero vectors z with real entries (z R)An square real symmetric matrix M is semi-positive defin ite if zT MZ e 0 for all non-zero vectors z with real entries (z R) An square real symmetric matrix M is semi-negative defi nite if zT MZ "d 0 for all non-zero vectors z with real entries (z R)" Example:The matrix is positive definite. For a vector with entries the ...

... c, x, y, z by equality of matrix we get a2 =0.0169, gives a=0.13, also ax=0.022659, ay=0.016159, putting the value of a in ax and ay we get x=0.1743, y=0.12 43, also x2 +y 2 +c 2 =0.01923, putting values of x and y, we get c =0.06a = 0.13, b=0.1934, c=0.06, x=0.1743, y=0.1243, z=0 .0134Putting the values in matrix we get ...

... Decomposition of a symmetric, positive-definite matrix into the product of a lower triangular matrix and its upper triangular matrix. The lower triangular matrix is the Cholesky triangle of the original, positive-definite matrixFor a given a Hermitian, positive-definite matrix A , Cholesky decomposition is unique Mathematically, If A has real entries and ...

... Example: For the matrix find the Eigen value and the corr esponding eigenvector ...

... www.edupristine.com © Neev Knowledge Management Pristine3 Both the equations reduce to the single linear equation x= To find an eigenvector, we choose any value for x (except 0), putting x=1 and setting y = x, we find the eigenvector to be Similar process for =1, leads to x = -y, hence the eigenvector ...