I am new to Bayesian regression models. I am trying to learn how to assess regression model assumptions while within the Bayesian context. Much of the literature is about implementing Bayesian models, but little to no information on model assumptions.

I am planning to get maximum a posteriori (MAP) estimates by using the parameter estimate modes from the posterior distribution. Then scoring the dataset using these values and doing residual checks for the model. However, I just came across my first hiccup, in that my posterior data set has no mode within the 10,000 observations (posterior dist is normally distributed, but all values are unique). I am just planning on rounding the estimates up and using the mode from those values. Does anyone have any suggestions or resources that may help me better understand checking model assumptions from Bayesian linear regression models.

Thanks!

I am planning to get maximum a posteriori (MAP) estimates by using the parameter estimate modes from the posterior distribution. Then scoring the dataset using these values and doing residual checks for the model. However, I just came across my first hiccup, in that my posterior data set has no mode within the 10,000 observations (posterior dist is normally distributed, but all values are unique). I am just planning on rounding the estimates up and using the mode from those values. Does anyone have any suggestions or resources that may help me better understand checking model assumptions from Bayesian linear regression models.

Thanks!

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