@@ -734,7 +734,7 @@ sample_size = 500_000
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734 | 734 | ar = LinearStateSpace(A_2, C_2, G_2, mu_0=np.ones(4)) |
735 | 735 | fig, ax = plt.subplots() |
736 | 736 | x, y = ar.simulate(sample_size) |
737 | | -mu_x, mu_y, Sigma_x, Sigma_y = ar.stationary_distributions() |
| 737 | +mu_x, mu_y, Sigma_x, Sigma_y, Sigma_yx = ar.stationary_distributions() |
738 | 738 | f_y = norm(loc=float(mu_y), scale=float(np.sqrt(Sigma_y))) |
739 | 739 | y = y.flatten() |
740 | 740 | ygrid = np.linspace(ymin, ymax, 150) |
@@ -920,7 +920,7 @@ def cross_plot(A,
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920 | 920 | ar = LinearStateSpace(A, C, G, mu_0=np.ones(4)) |
921 | 921 | |
922 | 922 | if steady_state == 'True': |
923 | | - μ_x, μ_y, Σ_x, Σ_y = ar.stationary_distributions() |
| 923 | + μ_x, μ_y, Σ_x, Σ_y, Σ_yx = ar.stationary_distributions() |
924 | 924 | ar_state = LinearStateSpace(A, C, G, mu_0=μ_x, Sigma_0=Σ_x) |
925 | 925 | |
926 | 926 | ymin, ymax = -0.6, 0.6 |
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