ProbabilidadJoint Distributions

Marginal and Conditional Densities

f_X(x) = ∫ f(x, y) dy, and X and Y are independent when f(x, y) = f_X(x) f_Y(y) everywhere.

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Esta lección aún no está traducida. Por ahora se muestra en inglés; la versión en español está en camino.

From a joint density we can recover the density of each variable alone and the density of one given the other, just as rows and columns did for joint tables. Sums become integrals.

Idea clave

$$f_X(x) = \int f(x, y)\,dy, \qquad f_{Y \mid X}(y \mid x) = \frac{f(x, y)}{f_X(x)},$$ integrating over the values of $y$ where $f(x, y) > 0$ for that $x$. $X$ and $Y$ are independent when $f(x, y) = f_X(x)\,f_Y(y)$ everywhere, which needs a support that is a rectangle.

Por qué funciona

$$P(a \le X \le b) = \int_{a}^{b}\left(\int f(x, y)\,dy\right)dx$$ so the inner integral is the density of $X$: integrate out $y$. Dividing a slice $f(x, \cdot)$ by its total $f_X(x)$ makes it a density in $y$, the conditional density. If the support is not a rectangle, some pairs $(x, y)$ are impossible although $x$ and $y$ are each possible alone; there $f(x, y) = 0$ while $f_X(x)f_Y(y) > 0$, so independence fails.
The density 2 on the triangle where x is below y. Each vertical slice runs from y equal to x up to 1, so its total, the marginal density of X, shrinks as x grows.

Worked examples

Ejemplo resuelto: A constant on a triangle

$f(x, y) = 2$ for $0 \le x \le y \le 1$. Find $f_X$, $f_Y$ and $f_{Y \mid X}(y \mid x)$.
  1. For fixed $x$, $y$ runs from $x$ to 1: $f_X(x) = \int_{x}^{1} 2\,dy = 2(1 - x)$ on $[0, 1]$.
  2. For fixed $y$, $x$ runs from 0 to $y$: $f_Y(y) = \int_{0}^{y} 2\,dx = 2y$.
  3. $f_{Y \mid X}(y \mid x) = \frac{2}{2(1 - x)} = \frac{1}{1 - x}$ for $x \le y \le 1$: given $X = x$, $Y$ is uniform on $[x, 1]$.

Respuesta: $2(1 - x)$, $2y$, and uniform on $[x, 1]$.

Ejemplo resuelto: A sum on the square

$f(x, y) = x + y$ on the unit square. Find $f_X$ and decide whether $X$ and $Y$ are independent.
  1. $f_X(x) = \int_{0}^{1}(x + y)\,dy = x + \frac{1}{2}$; by symmetry $f_Y(y) = y + \frac{1}{2}$.
  2. $$f_X(x)f_Y(y) = xy + \frac{x + y}{2} + \frac{1}{4} \ne x + y$$ (try $x = y = 0$: $\frac{1}{4} \ne 0$).

Respuesta: $f_X(x) = x + \frac{1}{2}$; dependent.

Ejemplo resuelto: A product that is not independent

$f(x, y) = 8xy$ for $0 \le x \le y \le 1$. Are $X$ and $Y$ independent?
  1. The formula is a product, but the support is a triangle.
  2. $f_X(x) = \int_{x}^{1} 8xy\,dy = 4x(1 - x^{2})$ and $f_Y(y) = \int_{0}^{y} 8xy\,dx = 4y^{3}$; their product is not $8xy$.
  3. Directly: $f(0.9, 0.5) = 0$ although $f_X(0.9) > 0$ and $f_Y(0.5) > 0$.

Respuesta: No.

Error común

Calling $f(x, y) = 2$ on the triangle $0 < x < y < 1$ "a product of separate functions" because it is constant. The support is not a rectangle, so knowing $X$ limits $Y$ (if $X = 0.9$ then $Y \ge 0.9$). Check two things: the formula splits as (a function of $x$) × (a function of $y$), and the support is a rectangle.

Práctica

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Fuentes y lecturas

  • MIT OpenCourseWare 18.05, Introduction to Probability and Statistics (Spring 2014), readings on marginal and conditional densities, independence
  • Grinstead and Snell, Introduction to Probability (2nd revised edition, American Mathematical Society; free from Dartmouth College), chapter 4 (Conditional Probability), section 4.2