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First of all, Bayesian approach is often used because you want to include prior knowledge in your model to enrich it. If you don't have any prior knowledge, then you stick to so-called "uninformative" or weekly informative priors. Notice that uniform prior is not "uninformative" by definition, since assumption about uniformity is an assumption. Yet there are cases where "it could be anything" is a reasonable "uninformative" prior assumption.

On another hand, Bayesian would argue that there is really no situations where you have no prior knowledge or beliefs whatsoever. You always can assume something and as a human being, you're doing it all the time. The whole Bayesian fuss with the priors is about quantifying those preconception and stating them explicitly in your model, since Bayesian inference is about updating your beliefs.

First of all, Bayesian approach is often used because you want to include prior knowledge in your model to enrich it. If you don't have any prior knowledge, then you stick to so-called "uninformative" or weekly informative priors. Notice that uniform prior is not "uninformative" by definition, since assumption about uniformity is an assumption. There is no such a thing as a truly uninformative prior. There are cases where "it could be anything" is a reasonable "uninformative" prior assumption, but there are also cases where stating that "all values are equally likely" is a very strong and unreasonable assumption. For example, if you assumed that my height can be anything between 0 centimeters and 3 meters, with all of the values being equally likely a priori, this wouldn't be a reasonable assumption and it would give too much weight to the extreme values, so it could possibly distort your posterior.

On another hand, Bayesian would argue that there is really no situations where you have no prior knowledge or beliefs whatsoever. You always can assume something and as a human being, you're doing it all the time (psychologists and behavioral economists made tons of research on this topic). The whole Bayesian fuss with the priors is about quantifying those preconception and stating them explicitly in your model, since Bayesian inference is about updating your beliefs.

It is easy to come up with "no prior assumptions" arguments, or uniform priors, for abstract problems, but for real-life problems you'd have prior knowledge. If you needed to make a bet about amount of money in an envelope, you'd know that the amount needs to be non-negative and finite. You also could make an educated guess about the upper bound for the possible amount of the money given your knowledge about the rules of the contest, funds available for your adversary, knowledge about physical size of the envelope and the amount of money that could physically fit in it, etc. You could also make some guesses about the amount of money that your adversary could be willing to put in the envelope and possibly loose. There is lots of things that you would know as a base for your prior.

First of all, Bayesian approach is often used because you want to include prior knowledge in your model to enrich it. If you don't have any prior knowledge, then you stick to so-called "uninformative" or weekly informative priors. Notice that uniform prior is not "uninformative" by definition, since assumption about uniformity is an assumption. Yet there are cases where "it could be anything" is a reasonable "uninformative" prior assumption.

On another hand, Bayesian would argue that there is really no situations where you have no prior knowledge or beliefs whatsoever. You always can assume something and as a human being, you're doing it all the time. The whole Bayesian fuss with the priors is about quantifying those preconception and stating them explicitly in your model, since Bayesian inference is about updating your beliefs.

UPDATE: Typo fixed

First of all, Bayesian approach is often used because you want to include prior knowledge in your model to enrich it. If you don't have any prior knowledge, then you stick to so-called "uninformative" or weekly informative priors. Notice that uniform prior is not "uninformative" by definition, since assumption about uniformity is an assumption. Yet there are cases where "it could be anything" is a reasonable "uninformative" prior assumption.

On another hand, Bayesian would argue that there is really no situations where you have no prior knowledge or beliefs whatsoever. You always can assume something and as a human being, you're doing it all the time. The whole Bayesian fuss with the priors is about quantifying those preconception and stating them explicitly in your model, since Bayesian inference is about updating your beliefs.

First of all, Bayesian approach is often used because you want to include prior knowledge in your model to enrich it. If you don't have any prior knowledge, then you stick to so-called "uninformative" or weekly informative priors. Notice that uniform prior is not "uninformative" by definition, since assumption about uniformity is an assumption. Yet there are cases where "it could be anything" is a reasonable "uninformative" prior assumption.

On another hand, Bayesian would argue that there is really no situations where you have no prior knowledge or beliefs whatsoever. You always can assume something and as a human being, you're doing it all the time. The whole Bayesian fuss with the priors is about quantifying those preconceptigoin and stating them explicitly in your model, since Bayesian inference is about updating your beliefs.

First of all, Bayesian approach is often used because you want to include prior knowledge in your model to enrich it. If you don't have any prior knowledge, then you stick to so-called "uninformative" or weekly informative priors. Notice that uniform prior is not "uninformative" by definition, since assumption about uniformity is an assumption. Yet there are cases where "it could be anything" is a reasonable "uninformative" prior assumption.

On another hand, Bayesian would argue that there is really no situations where you have no prior knowledge or beliefs whatsoever. You always can assume something and as a human being, you're doing it all the time. The whole Bayesian fuss with the priors is about quantifying those preconception and stating them explicitly in your model, since Bayesian inference is about updating your beliefs.

UPDATE: Typo fixed