How can I obtain the matrices of coefficients of quadratic form?

Question

I'm trying to find the matrices of coefficients of a quadratic form:

Clear["Global`*"]

x[1] = CDF[NormalDistribution[\[Mu], \[Sigma]], c];
x[2] = Expectation[x^2 \[Conditioned] x > c, 
   x \[Distributed] NormalDistribution[\[Mu], \[Sigma]]];
x[3] = 1 - CDF[NormalDistribution[\[Mu], \[Sigma]], c];

m = Table[a[i, j], {i, 1, 3}, {j, 1, 3}]
n = Table[b[i], {i, 1, 3}]

vars = Table[x[i], {i, 1, 3}]
fun[\[Mu]_, \[Sigma]_, c_, o_] =vars.m.vars + n.vars + o

Suppose that all I observe is the expression for $\text{fun}$. I want to express it in matrix form in order to easily perform sums with other similar quadratic expressions. That is, I want to find $\{\tilde{m}, \tilde{n},\tilde{o}\}$ such that $\text{fun(μ,σ,c,o)} ≡ \text{vars}^{\prime} \tilde{m} \text{vars} +\text{vars}^{\prime} \tilde{n} + \tilde{o}$. My attempt using CoefficientArrays has not been successful:

{oo, nn,mm} = 
  Normal@CoefficientArrays[fun[\[Mu], \[Sigma], c, o], vars];

Any ideas on how to do that?

Answer 1

For some reason, those functions are not recognized as variables by CoefficientArrays. So, the following trick ended up by useful:

fun2[\[Mu]_, \[Sigma]_, c_,o_] = fun[\[Mu], \[Sigma], c,o]//. x[3] -> xx[3] //. x[2] -> xx[2] //. x[1] -> xx[1];
vars2=Table[xx[i], {i, 1, 3}];
{oo, nn,mm} = 
  Normal@CoefficientArrays[fun2[\[Mu], \[Sigma], c,o],vars2];

Verifying it:

vars.mm.vars + nn.vars + oo == fun[\[Mu], \[Sigma], c,o] // Simplify
(* True *)