What's the intuition for ABA' in linear algebra? I've seen the pattern ABA', where A and B are matrices, and ' stands for the transpose, many times so I want to know if there is an intuition for this pattern.
I did some development to see what the resulting matrix would look like and I got this:
Let A be a matrix of dimensions (l,m),
    B be a matrix of dimensions (m,m) and
    R = ABA' with dimensions (l,l)
Then:




*

*Is my development correct?

*When does this pattern occur? (I've seen
it a lot in machine learning).


One context where I've seen this pattern is in the equations of the Kaman filter (taken from wikipedia) :

 A: This pattern often occurs when there is, explicitly or implicitly, an orthonormal change of basis going on.
If we have a matrix $B$ and would like to re-express the transformation of multiplication by $B$ in another basis, standard linear algebra tells us that the matrix expression of the same transformation in the new basis is a similar matrix:
$$ B_{\text{new}} = A B A^{-1} $$
Here the matrix $A$ is called a change of basis matrix.
When we want to change between two orthonormal basies, a simple calculation shows that the change of basis matrix is an orthogonal matrix.  Orthogonal matrices $B$ satisfy the identity:
$$ B^{-1} = B' $$
So this is how you end up with expressions like $B A B'$.
A: For short
This is covariance matrix's property $var(AX)=Avar(X)A^T$.
Also in scalar variance, $var(aX) = a^2var(X)$ when $a$ is constant. (Maybe) we can interprete as squred of matrix as well.
In other , we can interprete this and linear tranformation of ramdom variable inside covariance matrix. when we find $ABA^T$. and we know that $B=Var(X)$ so $ABA^T = Var(AX)$
For long answer.
I learning about Kalman filter recently, so I would like to explain with covariance perspective.

*

*From Kalman filter state transition equation.

$$x_k = Fx_{k-1}+Bu_k+w_k$$
Which define new state from last state and control input, we can predect next state (step k) given last step (step k-1) data by
$$\hat{x}_{k|k-1} = F\hat{x}_{k-1|k-1}+Bu_k$$
1.1 Then we find error  covariance matrix of $\hat{x}_{k|k-1}$ called $P_{k|k-1}$ by
$$
P_{k|k-1}=Var(x_k-\hat{k}_{x|k-1})
$$
$$
P_{k|k-1}=Var((Fx_{k-1}+Bu_k+w_k)-(F\hat{x}_{k-1|k-1}+Bu_k))
$$
$$
P_{k|k-1}=Var(F(x_{k-1}-\hat{x}_{k-1|k-1})+w_k)
$$
1.2 With covariance matrix property, $Var(A\pm B)=Var(A)+Var(B)$ when A and B are independent. we got
$$P_{k|k-1}=Var(F(x_{k-1}-\hat{x}_{k-1|k-1}))+Var(w_k)$$
and we know that $Var(w_k) = Q_k$
$$P_{k|k-1}=Var(F(x_{k-1}-\hat{x}_{k-1|k-1}))+Q_k$$
1.2 when $F$ is constant matrix, with caovaraince matrix's property $var(AX)=Avar(X)A^T$, then we got
$$P_{k|k-1}=F Var(x_{k-1}-\hat{x}_{k-1|k-1})F^T+Q_k$$
1.3 we know that $P_{k-1|k-1}=Var(x_{k-1}-\hat{x}_{k-1|k-1})$, we got
$$P_{k|k-1}=F P_{k-1|k-1}F^T+Q_k$$
You see that $F P_{k-1|k-1}F^T$ pattern is just for update covariance matrix into new state coresponding to state update.
Example of $F$ and $H$
this is example value from wikipedia
$$F=
\begin{bmatrix}
1 & \Delta t\\
0 & 1 
\end{bmatrix}
$$
$$H=
\begin{bmatrix}
1 & 0\\
\end{bmatrix}
$$
