7.5. Two Variables#

Let X and Y be two random variables and let F(X,Y)(x,y) be their joint CDF.

limxyFX,Y(x,y)=0.
limxyFX,Y(x,y)=1.

Right continuity:

limxx0+FX,Y(x,y)=FX,Y(x0,y).
limyy0+FX,Y(x,y)=FX,Y(x,y0).

The joint probability density function is given by fX,Y(x,y). It satisfies fX,Y(x,y)0 and

fX,Y(x,y)dydx=1.

The joint CDF and joint PDF are related by

FX,Y(x,y)=P(Xx,Yy)=xyfX,Y(u,v)dvdu.

Further

P(aXb,cYd)=abcdfX,Y(u,v)dvdu.

The marginal probability is

P(aXb)=P(aXb,Y)=abfX,Y(u,v)dvdu.

We define the marginal density functions as

fX(x)=fX,Y(x,y)dy

and

fY(y)=fX,Y(x,y)dx.

We can now write

P(aXb)=abfX(x)dx.

Similarly

P(cYd)=cdfY(y)dy.

7.5.1. Conditional Density#

We define

P(axb|y=c)=abfX|Y(x|y=c)dx.

We have

fX|Y(x|y=c)=fX,Y(x,c)fY(c).

In other words

fX|Y(x|y=c)fY(c)=fX,Y(x,c).

In general we write

fX|Y(x|y)fY(y)=fX,Y(x,y).

Or even more loosely as

f(x|y)f(y)=f(x,y).

More identities

f(x|yd)=df(x,y)dyP(yd).

7.5.2. Independent Variables#

If X and Y are independent then

fX,Y(x,y)=fX(x)fY(y).
f(x|y)=f(x,y)f(y)=f(x)f(y)f(y)=f(x).

Similarly

f(y|x)=f(y).

The CDF also is separable

FX,Y(x,y)=FX(x)FY(y).