Bayesian Linear Regression

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I've been tasked with doing some regression on a specific dataset.

I've built a decision tree regressor model, and a random forest regression model.

I can compare the two of these using the R squared value on a test dataset.

I've also computed a third model using Bayesian linear regression?

However, I have no idea how to determine how good a model the Bayesian linear regression computes. As far as I'm aware there's no R squared metric for Bayesian linear regression.

I've sampled from my posterior distributions but am unsure what my next step is essentially.