# 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

$f(y_{1}|y_{2})*f(y_{2})=f(y_{1},y_{2})=f(y_{2}|y_{1})*f(y_{1})$
• Thank you! What exactly do $f(y_{1})$ and $f(y_{2})$ represent? And is there a way to find them if all I have is the conditional distribution? – F. Stephen Q Nov 19 '14 at 5:01