What’s the difference between Bayesian and Frequentist marketing mix models?

I see a lot of debate. Some of it is frustrating about Bayesian versus frequentist approaches to lots of different things, but particularly in the field of media mix modeling. Given your background, your expertise in statistics and marketing science, I'd love for you to talk a little bit about what people mean when they talk about Bayesian versus frequentist approaches, where that matters, where does it not matter?

From a fundamental perspective, what people are talking about is different ways of thinking about uncertainty in the, in the models we're building. Fundamentally, when I say uncertainty, I, I do mean kind of intuitively what you think of when I say uncertain. The future is uncertain, or the effectiveness of what we're putting our dollars into is uncertain. The question is, how do we quantify, how do we think about that uncertainty and what it means, and how do we make decisions based on that uncertainty? This is sort of the philosophical difference between the Bayesian worldview and the, the frequentist worldview, thinking about what does uncertainty mean and, and how do we quantify it in a statistical model, being dogmatically Bayesian versus dogmatically frequentist. I don't necessarily think this is the right way to be thinking, although I tend to fall on one side more than the other. I think instead you can ask yourself what view on uncertainty and what kinds of models make sense in the context that I care about. From that perspective, makes a lot of sense in the context of MMs. When we think about uncertainty from a Bayesian perspective, once you have the model in hand, the uncertainty kind of just falls out from that model. So you've got your set of assumptions, you take those assumptions, combine them with data, and you get some estimates of how effective is this marketing channel, or what does the future look like if I spend X. From the frequentist perspective, the idea of uncertainty is a little different. It is often thinking about, say, If I could hypothetically go back and get a new sample of data and refit this model again and again, how would the parameters of the model change given these independent draws of hypothetical random datasets?