On the (dis)similarities between stationary imprecise and non-stationary precise uncertainty models in algorithmic randomness

The field of algorithmic randomness studies, amongst other things, what it means for infinite binary sequences to be random some given uncertainty model. Classically, martingale-theoretic notions involve precise models, and is only recently that imprecision has been introduced into this context. As a consequence, the investigation how alters our view on just begun. In contribution, where we allow non-computable establish close surprising connection between imprecise models in particular, show there are stationary non-stationary have exact same set sequences. We also give preliminary discussion possible implications result statistics based probabilities, shed light practical (ir)relevance both