The structure of the concentration and covariance matrix in a naive Bayes model

In Ryacas: R Interface to the 'Yacas' Computer Algebra System

knitr::opts_chunk$set(echo = TRUE)

library(Ryacas)

Naive Bayes model

Consider this model: $$ x_i = a x_0 + e_i, \quad i=1, \dots, 4 $$ and $x_0=e_0$. All terms $e_0, \dots, e_3$ are independent and $N(0,1)$ distributed. Let $e=(e_0, \dots, e_3)$ and $x=(x_0, \dots x_3)$. Isolating error terms gives that $$ e = L_1 x $$ where $L_1$ has the form

L1chr <- diag(4)
L1chr[2:4, 1] <- "-a"
L1 <- ysym(L1chr)
L1

If error terms have variance $1$ then $\mathbf{Var}(e)=L \mathbf{Var}(x) L'$ so the covariance matrix is $V1=\mathbf{Var}(x) = L^- (L^-)'$ while the concentration matrix (the inverse covariances matrix) is $K=L' L$.

L1inv <- solve(L1)
K1 <- t(L1) %*% L1
V1 <- L1inv %*% t(L1inv)

cat(
  "\\begin{align} 
    K_1 &= ", tex(K1), " \\\\ 
   V_1 &= ", tex(V1), " 
  \\end{align}", sep = "")

Slightly more elaborate:

L2chr <- diag(4)
L2chr[2:4, 1] <- c("-a1", "-a2", "-a3")
L2 <- ysym(L2chr)
L2
Vechr <- diag(4)
Vechr[cbind(1:4, 1:4)] <- c("w1", "w2", "w2", "w2")
Ve <- ysym(Vechr)
Ve

L2inv <- solve(L2)
K2 <- t(L2) %*% solve(Ve) %*% L2
V2 <- L2inv %*% Ve %*% t(L2inv)

cat(
  "\\begin{align} 
    K_2 &= ", tex(K2), " \\\\ 
   V_2 &= ", tex(V2), " 
  \\end{align}", sep = "")

Ryacas documentation built on Jan. 17, 2023, 1:11 a.m.