Expand (x-μi)ᵀΣ⁻¹(x-μi) Homework

  • Thread starter Thread starter devonho
  • Start date Start date
  • Tags Tags
    Expanding
Click For Summary
SUMMARY

The discussion focuses on expanding the expression (\bf{x-\mu_{i}})^{t}\Sigma^{-1}(\bf{x-\mu_{i}}), where \bf{x} and \bf{\mu_{i}} are column vectors and \Sigma is a square matrix. The correct expansion is confirmed as \bf{x}^t\Sigma^{-1}\bf{x} + \bf{\mu_{i}}^t\Sigma^{-1}\bf{\mu_{i}} - 2\bf{x}^t\Sigma^{-1}\bf{\mu_{i}}. Additionally, it is established that \bf{\mu_{i}}^t\Sigma^{-1}\bf{x} can be expressed as \bf{x}^t\Sigma^{-1}\bf{\mu_{i}} if \Sigma is symmetric, which is typical for variance-covariance matrices.

PREREQUISITES
  • Understanding of matrix algebra, specifically transposition and inverse operations.
  • Familiarity with variance-covariance matrices in statistics.
  • Knowledge of vector notation and operations involving column vectors.
  • Basic principles of linear algebra, including properties of symmetric matrices.
NEXT STEPS
  • Study the properties of symmetric matrices and their implications in linear algebra.
  • Learn about variance-covariance matrices and their applications in statistics.
  • Explore matrix operations, particularly focusing on transposition and inversion.
  • Investigate the derivation of quadratic forms in multivariate statistics.
USEFUL FOR

Students and professionals in mathematics, statistics, and data science who are working with multivariate analysis and require a solid understanding of matrix operations and their applications in statistical models.

devonho
Messages
8
Reaction score
0

Homework Statement



I need help with expanding:

(\bf{x-\mu_{i}})^{t}\Sigma^{-1}(\bf{x-\mu_{i}})

\bf{x,\mu_{i}} are column vectors.
\Sigma is a square matrix.

Thank you.

Homework Equations



Can:

<br /> \bf{x}^t\Sigma^{-1}\bf{\mu_{i}}<br />

be written as?

<br /> \bf{\mu_{i}}^t\Sigma^{-1}\bf{x}<br />

I've tried to compute this numerically and the answer is no.


The Attempt at a Solution



<br /> (\bf{x-\mu_{i}})^{t}\Sigma^{-1}(\bf{x-\mu_{i}})<br /> =<br /> \bf{x}^t\Sigma^{-1}\bf{x}+<br /> \bf{\mu_{i}}^t\Sigma^{-1}\bf{\mu_{i}}-<br /> \bf{x}^t\Sigma^{-1}\bf{\mu_{i}}-<br /> \bf{\mu_{i}}^t\Sigma^{-1}\bf{x}<br />

 
Physics news on Phys.org


devonho said:

Homework Statement



I need help with expanding:

(\bf{x-\mu_{i}})^{t}\Sigma^{-1}(\bf{x-\mu_{i}})

\bf{x,\mu_{i}} are column vectors.
\Sigma is a square matrix.

Thank you.

Homework Equations



Can:

<br /> \bf{x}^t\Sigma^{-1}\bf{\mu_{i}}<br />

be written as?

<br /> \bf{\mu_{i}}^t\Sigma^{-1}\bf{x}<br />

I've tried to compute this numerically and the answer is no.


The Attempt at a Solution



<br /> (\bf{x-\mu_{i}})^{t}\Sigma^{-1}(\bf{x-\mu_{i}})<br /> =<br /> \bf{x}^t\Sigma^{-1}\bf{x}+<br /> \bf{\mu_{i}}^t\Sigma^{-1}\bf{\mu_{i}}-<br /> \bf{x}^t\Sigma^{-1}\bf{\mu_{i}}-<br /> \bf{\mu_{i}}^t\Sigma^{-1}\bf{x}<br />

I don't understand your problem. The result in (3) is correct. If *you* derived that result, then you have obtained the desired result. On the other hand, if you mean that somebody else has given you the result in (3) and you don't know how they got it, that is a different question. So, what, exactly are you asking?

RGV
 


BTW, \Sigma is a horrible name for a matrix for the reason that it is used primarily to mean summation.
 


Mark44 said:
BTW, \Sigma is a horrible name for a matrix for the reason that it is used primarily to mean summation.

I agree with you. Nevertheless, it is often used in Statistics and Econometrics, etc., to denote the variance-covariance matrix of a multidimensional random variable.

RGV
 


devonho said:

Homework Statement




Can:

<br /> \bf{x}^t\Sigma^{-1}\bf{\mu_{i}}<br />

be written as?

<br /> \bf{\mu_{i}}^t\Sigma^{-1}\bf{x}<br />

I've tried to compute this numerically and the answer is no.

The answer is yes IF the matrix \Sigma is symmetric. Since
<br /> \bf{\mu_i}^t \Sigma^{-1} \bf{x}<br />

is a scalar, it equals its transpose, so
<br /> \bf{\mu_i}^t \Sigma^{-1} \bf{x} = \left(\bf{\mu_i}^t \Sigma^{-1} \bf{x}\right)^t = \bf{x}^t \Sigma^{-1} \bf{\mu_i}<br />

Did the example you used have sigma symmetric? (If it is a variance-covariance matrix, it has to be symmetric).
 


The way (3) is written, it is valid even if the matrix is not symmetric: the terms x^T A m and m^T A x are written separately.

RGV
 


Hi all, thanks for the replies. The goal was to get:

(\bf{x-\mu_{i}})^{t}\Sigma^{-1}(\bf{x-\mu_{i}}) <br /> = \bf{x}^t\Sigma^{-1}\bf{x}+ \bf{\mu_{i}}^t\Sigma^{-1}\bf{\mu_{i}}- 2\bf{x}^t\Sigma^{-1}\bf{\mu_{i}}

Hence,
<br /> \bf{\mu_i}^t \Sigma^{-1} \bf{x} = \bf{x}^t \Sigma^{-1} \bf{\mu_i}<br />

Was what I needed. Thanks.
 


Thanks for the help. I wrote the long proof.

If <br /> m_{12}=m_{21}, m_{13}=m_{31},m_{32}=m_{23},<br />

<br /> \bf{x} = \left[<br /> \begin{array}{ccc}<br /> x_1 \\ x_2 \\ x_3<br /> \end{array}<br /> \right]<br />

<br /> \bf{y} = \left[<br /> \begin{array}{ccc}<br /> y_1 \\ y_2 \\ y_3<br /> \end{array}<br /> \right]<br />

<br /> \bf{M} = <br /> \left[<br /> \begin{array}{ccc}<br /> m_{11} &amp; m_{12} &amp; m_{13} \\<br /> m_{21} &amp; m_{22} &amp; m_{23} \\<br /> m_{31} &amp; m_{32} &amp; m_{33}<br /> \end{array}<br /> \right]<br />


then

<br /> \bf{y^tMx}=<br />
<br /> \begin{array}{ccc}<br /> x_1y_1m_{11}+<br /> (x_1y_2+x_2y_1)m_{12}+<br /> (x_1y_3 + x_3y_1)m_{13}+<br /> x_2y_2m_{22}+<br /> (x_2y_3 + x_3y_2)m_{23}+<br /> x_3y_3m_{33}<br /> \end{array}<br />

<br /> \bf{x^tMy}=<br />
<br /> \begin{array}{ccc}<br /> \begin{array}{ccc}<br /> (x_1m_{11}+x_2m_{21}+x_3m_{31})y_1 + (x_1m_{12}+x_2m_{22}+x_3m_{32})y_2 + (x_1m_{13}+x_2m_{23}+x_3m_{33})y_3<br /> \end{array}<br /> \\=<br /> \begin{array}{ccc}<br /> x_1y_1m_{11}+<br /> (x_2y_1+<br /> x_1y_2)m_{12}+<br /> (x_3y_1+ <br /> x_1y_3)m_{13}+<br /> x_2y_2m_{22}+<br /> (x_3y_2+ <br /> x_2y_3)m_{23}+<br /> x_3y_3m_{33}<br /> \end{array}<br /> \end{array}<br />
<br /> =\bf{y^tMx}<br />
 
Last edited:

Similar threads

  • · Replies 4 ·
Replies
4
Views
1K
  • · Replies 13 ·
Replies
13
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 4 ·
Replies
4
Views
10K
  • · Replies 41 ·
2
Replies
41
Views
6K
  • · Replies 4 ·
Replies
4
Views
1K
Replies
7
Views
3K
Replies
6
Views
1K