Finding density functions from conditional distribution I'm currently taking a statistics course, but I'm having trouble with a specific concept, and hope this is a good place to ask.

Essentially, for random variables $y_{1},y_{2}$, how do you get from the conditional distribution function 

$f(y_{1}|y_{2})=something$ 

to the joint density function 

$f(y_{1},y_{2})$

or the marginal density function? Is there an general algorithm/relationship for converting between these?  

Thank you
yes:

$f(y_{1}|y_{2})*f(y_{2})=f(y_{1},y_{2})=f(y_{2}|y_{1})*f(y_{1})$
yes:

$f(y_{1}|y_{2})*f(y_{2})=f(y_{1},y_{2})=f(y_{2}|y_{1})*f(y_{1})$