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Suppose that the joint p.d.f. of two random variables X and Y is as follows:

\(f\left( {x,y} \right) = \left\{ \begin{aligned}{l}c\left( {x + {y^2}} \right)\,\,\,\,\,\,for\,0 \le x \le 1\,and\,0 \le y \le 1\\0\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,otherwise\end{aligned} \right.\)

Determine

(a) the conditional p.d.f. of X for every given value of Y, and

(b) \({\rm P}\left( {X > \frac{1}{2}|Y = \frac{3}{2}} \right)\).

Short Answer

Expert verified
  1. The conditional pdf of x for every value of y is \({g_1}\left( {x|y} \right) = \left\{ {\frac{{x + {y^2}}}{{\frac{1}{2} + {y^2}}}\,for\,\;} \right.0 \le x \le 1\,and\,0 \le y \le 1\)
  2. \(\frac{1}{3}\)

Step by step solution

01

Given information

The joint pdf of two random variables X and Y:

\(f\left( {x,y} \right) = \left\{ \begin{aligned}{l}c\left( {x + {y^2}} \right)\,\,\,\,\,\,for\,0 \le x \le 1\,and\,0 \le y \le 1\\0\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,otherwise\end{aligned} \right.\)

02

Calculating Conditional pdf

a)

Marginal pdf of Y For \(0 \le y \le 1\):

\(\begin{aligned}{f_2}\left( y \right) = \int\limits_0^1 {f\left( {x,y} \right)dx} \\ = c\left( {\frac{1}{2} + {y^2}} \right).\end{aligned}\)

Therefore, for\(0 \le x \le 1\)and\(0 \le y \le 1\)the conditional pdf of X given that Y=y:

\(\begin{aligned}{g_1}\left( {x|y} \right) = \frac{{f\left( {x,y} \right)}}{{{f_2}\left( y \right)}}\\ = \frac{{x + {y^2}}}{{\frac{1}{2} + {y^2}}}\end{aligned}\) .

Hence,

\({g_1}\left( {x|y} \right) = \left\{ {\frac{{x + {y^2}}}{{\frac{1}{2} + {y^2}}}\,for\,\;} \right.0 \le x \le 1\,and\,0 \le y \le 1\)

03

When: \({\rm P}\left( {X > \frac{1}{2}|Y = \frac{3}{2}} \right)\)

b)

When\(Y = {\raise0.7ex\hbox{$1$} \!\mathord{\left/

{\vphantom {1 2}}\right.\kern-\nulldelimiterspace}

\!\lower0.7ex\hbox{$2$}}\)it follows part a, which means

\({g_1}\left( {x|y = \frac{1}{2}} \right) = \left\{ \begin{aligned}{l}\frac{4}{3}\left( {x + \frac{1}{4}} \right)\,\,\,\,\,for\,0 \le x \le 1\\0\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,othrwise\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\end{aligned} \right.\,\,\,\,\)

Therefore,

\(\begin{aligned}{\rm P}\left( {X < \frac{1}{2}|Y = \frac{1}{2}} \right) = \int\limits_0^{\frac{1}{2}} {{g_1}\left( {x|y = \frac{1}{2}} \right)} dx\\ = \frac{1}{3}\end{aligned}\)

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Most popular questions from this chapter

Let Y be the rate (calls per hour) at which calls arrive at a switchboard. Let X be the number of calls during a two-hour period. Suppose that the marginal p.d.f. of Y is

\({{\bf{f}}_{\bf{2}}}\left( {\bf{y}} \right){\bf{ = }}\left\{ {\begin{align}{}{{{\bf{e}}^{{\bf{ - y}}}}}&{{\bf{if}}\,{\bf{y > 0,}}}\\{\bf{0}}&{{\bf{otherwise,}}}\end{align}} \right.\)

And that the conditional p.d.f. of X given\({\bf{Y = y}}\)is

\({{\bf{g}}_{\bf{1}}}\left( {{\bf{x}}\left| {\bf{y}} \right.} \right){\bf{ = }}\left\{ {\begin{align}{}{\frac{{{{\left( {{\bf{2y}}} \right)}^{\bf{x}}}}}{{{\bf{x!}}}}{{\bf{e}}^{{\bf{ - 2y}}}}}&{{\bf{if}}\,{\bf{x = 0,1,}}...{\bf{,}}}\\{\bf{0}}&{{\bf{otherwise}}{\bf{.}}}\end{align}} \right.\)

  1. Find the marginal p.d.f. of X. (You may use the formula\(\int_{\bf{0}}^\infty {{{\bf{y}}^{\bf{k}}}{{\bf{e}}^{{\bf{ - y}}}}{\bf{dy = k!}}} {\bf{.}}\))
  2. Find the conditional p.d.f.\({{\bf{g}}_{\bf{2}}}\left( {{\bf{y}}\left| {\bf{0}} \right.} \right)\)of Y given\({\bf{X = 0}}\).
  3. Find the conditional p.d.f.\({{\bf{g}}_{\bf{2}}}\left( {{\bf{y}}\left| 1 \right.} \right)\)of Y given\({\bf{X = 1}}\).
  4. For what values of y is\({{\bf{g}}_{\bf{2}}}\left( {{\bf{y}}\left| {\bf{1}} \right.} \right){\bf{ > }}{{\bf{g}}_{\bf{2}}}\left( {{\bf{y}}\left| {\bf{0}} \right.} \right)\)? Does this agree with the intuition that the more calls you see, the higher you should think the rate is?

Suppose that the joint p.d.f. of two points X and Y chosen by the process described in Example 3.6.10 is as given by Eq. (3.6.15). Determine (a) the conditional p.d.f.of X for every given value of Y , and (b)\({\rm P}\left( {X > \frac{1}{2}|Y = \frac{3}{4}} \right)\)

For the conditions of Exercise 1, find the p.d.f. of the

average \(\frac{{\left( {{{\bf{X}}_{\bf{1}}}{\bf{ + }}{{\bf{X}}_{\bf{2}}}} \right)}}{{\bf{2}}}\)

Let X and Y be random variables for which the jointp.d.f. is as follows:

\({\bf{f}}\left( {{\bf{x,y}}} \right){\bf{ = }}\left\{ \begin{array}{l}{\bf{2}}\left( {{\bf{x + y}}} \right)\;\;\;\;\;\;\;\;\;\;{\bf{for}}\;{\bf{0}} \le {\bf{x}} \le {\bf{y}} \le {\bf{1,}}\\{\bf{0}}\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;{\bf{otherwise}}\end{array} \right.\)

Find the p.d.f. of Z = X + Y.

Question:Suppose thatXandYhave a continuous joint distribution for which the joint p.d.f. is

\({\bf{f}}\left( {{\bf{x,y}}} \right){\bf{ = }}\left\{ \begin{array}{l}{\bf{k}}\;{\bf{for}}\;{\bf{a}} \le {\bf{x}} \le {\bf{b}}\;{\bf{and}}\;{\bf{c}} \le {\bf{y}} \le {\bf{d}}\\{\bf{0}}\;{\bf{otherwise}}\end{array} \right.\)

wherea <b,c < d, andk >0.

Find the marginal distributions ofXandY.

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