For random variable that defined using a Flat distribution, you can understand it as a free parameter that follow a uniform distribution on (-inf, inf). It’s also called an uninformative prior (some what misleading as Flat prior is also informative under some transformation).
In most case we dont recommend using it as using a Normal distribution with large sigma (i.e., a weakly informative prior) is usually much better re model convergence.
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