Question about tutorial: Model fitting with correlated noise

[Whitehaawk]

Its possible that im dumb, but I am just wondering why in the final fit the truth dict is used for constructing the model

kwargs = dict(**truth) kwargs["bounds"] = dict(location=(-2, 2)) mean_model = Model(**kwargs)

[dfm]

In that step, we're initializing the model parameters. I agree that it's a bit convoluted to initialize at the truth, but it was the easiest option for the tutorial. They will then get updated when optimizing and sampling so the results aren't just showing the truth!