cond_indep_normal

hyppo.tools.cond_indep_normal(n, p=1, random_state=None)

Conditionally independent normal distributions.

\((X, Y, Z) \in \mathbb{R} \times \mathbb{R} \times \mathbb{R}\): .. math:

\mu &= (0, 0, 0)\\
\Sigma &= \begin{bmatrix}
1 & 0.36 & 0.6 \\
0.36 & 1 & 0.6 \\
0.6 & 0.6 & 1
\end{bmatrix}\\
(X, Y, Z) &\sim MVN(\mu, \Sigma)

The conditional covariance matrix is given by: .. math:

\Sigma(X, Y | Z) &= \begin{bmatrix}
0.64 & 0\\
0 & 0.64
\end{bmatrix}

Parameters

• n (int) -- The number of samples desired by the simulation (>= 5).

• p (int) -- Ignored.

Returns

x,y,z (ndarray of float) -- Simulated data matrices. x, y, and z.

References

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indep_binomial

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cond_indep_lognormal