Bayesian statistics vs frequentist methods What are the advantages of Bayesian statistics over traditional (frequentist) methods?
The link below is really helpful, but the topic still seems controversial and complicated to me. Can someone put it simple?
When are Bayesian methods preferable to Frequentist? 
 A: Bayesian statistics can be applied to most statistical questions (I won't say all because it's impossible to know, but all the stuff that commonly comes up on the frequentist side can be done in a Bayesian context).
The advantages of Bayesian statistics include the ability to directly sample the distribution of the parameter of interest, and the ability to model complex, intermixed random variables that make up a single system. You can also encode prior information in your models. 
The assumptions tend to be stronger than frequentist statistics as a result. You have to have some prior information, which is really another parameter to figure out. You also spend a lot of time figuring out how to sample from intractable distributions (MC, VI).
A: 
Can Bayesian analysis be applied to any type of statistical question?

I think the answer is "yes", but care should be taken.  With enough data, the results between bayesian and frequenstist methods start to look very similar (that is, unless you have unreasonably strong priors).

What are the advantages of Bayesian statistics over traditional (frequentist) methods and its main assumptions?

That you can incorporate prior information into your models is a huge advantage.  The prior information let's you "hit the ground running" so to speak.
You can create very complex models to model most any phenomenon.  I use Bayesian statistics to model drug disposition between patients.  This not only lets me understand how a particular patient may metabolize a drug, but also how a new patient (one which I may have not measured yet) may metabolize a drug.
However, Bayesianism is very complex and not something you can just pick up.  The processes of model validation is more involved than frequenstist stats.  
