Expand (x-μi)ᵀΣ⁻¹(x-μi) Homework

  • Thread starter Thread starter devonho
  • Start date Start date
  • Tags Tags
    Expanding
AI Thread Summary
The discussion focuses on expanding the expression (x-μi)ᵀΣ⁻¹(x-μi), where x and μi are column vectors and Σ is a square matrix. The correct expansion is confirmed to be xᵀΣ⁻¹x + μiᵀΣ⁻¹μi - 2xᵀΣ⁻¹μi. There is a query about whether xᵀΣ⁻¹μi can be expressed as μiᵀΣ⁻¹x, which is affirmed to be true if Σ is symmetric, as both expressions yield the same scalar value. The conversation also touches on the notation of Σ and its common use in statistics as a variance-covariance matrix. The final confirmation of the expansion and the relationship between the terms provides clarity on the mathematical properties involved.
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:
Back
Top