diff --git a/docs/posts/dr-dfm-covid/presentation-post.html b/docs/posts/dr-dfm-covid/presentation-post.html index 2a57bda..7b05c61 100644 --- a/docs/posts/dr-dfm-covid/presentation-post.html +++ b/docs/posts/dr-dfm-covid/presentation-post.html @@ -136,10 +136,7 @@
Examining the first transition matrix
First Transition Matrix
Second Transition Matrix
By applying constraints to the model parameters, we can improve interpretability and reduce complexity while incorporating prior domain knowledge about variable relationships.
diff --git a/posts/dr-dfm-covid/index.qmd b/posts/dr-dfm-covid/index.qmd index 2bd28bc..a96a760 100644 --- a/posts/dr-dfm-covid/index.qmd +++ b/posts/dr-dfm-covid/index.qmd @@ -136,8 +136,6 @@ $\eta_t$: Innovation term ## Interpreting Transition Matrices -Examining the first transition matrix - ```{python} #| echo: false import numpy as np @@ -170,12 +168,15 @@ plt.tight_layout() plt.show() ``` +**First Transition Matrix** + - The diagonal elements (0.8 and 0.7) are relatively high, indicating a strong persistence of each latent factor over time. - The off-diagonal elements (0.2 and 0.3) suggest moderate influence of one latent factor on the other, allowing for some interaction between the two factors. - Summary: latent factors have a tendency to persist, with some interdependence. -### Examining the second transition matrix +**Second Transition Matrix** + - The diagonal elements (0.5 and 0.4) are lower compared to Transition Matrix 1, suggesting less persistence of each latent factor over time. - The off-diagonal elements (0.5 and 0.6) indicate a relatively stronger influence of one latent factor on the other compared to Transition Matrix 1. - Summary: latent factors are less likely to persist and may be influenced more by each other, allowing for a more dynamic and responsive behavior.