Class: CompactClassificationDiscriminant
Log unconditional probability density for discriminant analysis classifier
lp = logP(obj,Xnew)
returns
the log of the unconditional probability density of each row of lp
= logP(obj
,Xnew
)Xnew
,
computed using the discriminant analysis model obj
.

Discriminant analysis classifier, produced using 

Matrix where each row represents an observation, and each column
represents a predictor. The number of columns in 

Column vector with the same number of rows as 
The unconditional probability density of a point x of a discriminant analysis model is
$$P(x)={\displaystyle \sum _{k=1}^{K}P(x,k),}$$
where P(x,k) is the conditional density of the model at x for class k, when the total number of classes is K.
The conditional density P(x,k) is
P(x,k) = P(k)P(xk),
where P(k) is the prior probability of class k, and P(xk) is the conditional density of x given class k. The conditional density function of the multivariate normal with mean μ_{k} and covariance Σ_{k} at a point x is
$$P\left(xk\right)=\frac{1}{{\left(2\pi \left{\Sigma}_{k}\right\right)}^{1/2}}\mathrm{exp}\left(\frac{1}{2}{\left(x{\mu}_{k}\right)}^{T}{\Sigma}_{k}^{1}\left(x{\mu}_{k}\right)\right),$$
where $$\left{\Sigma}_{k}\right$$ is the determinant of Σ_{k}, and $${\Sigma}_{k}^{1}$$ is the inverse matrix.