Linear models and high autocorrelation

Linear models lead to posterior distribution where and are highly correlated. See the following code and Figure 3.4 for an example:

az.plot_pair(trace_g, var_names=['α', 'β'], plot_kwargs={'alpha': 0.1})
Figure 3.4

The correlation we are seeing in Figure 3.4 is a direct consequence of our assumptions. No matter which line we fit to our data, all of them should pass for one point, that is, the mean of the ...

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