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density function (Definition)

Let $ X$ be a discrete random variable with sample space $ \{x_1,x_2,\ldots\}$. Let $ p_k$ be the probability of $ X$ taking the value $ x_k$.

The function

\begin{displaymath} f(x)= \begin{cases} p_k & \text{if }x=x_k\ 0 & \text{otherwise} \end{cases}\end{displaymath}
is called the probability function or density function.

It must hold:

$\displaystyle \sum_{j=1}^{\infty} f(x_j)=1$

If the density function for a random variable is known, we can calculate the probability of $ X$ being on certain interval:

$\displaystyle P[a<X\leq b] = \sum_{a<x_j\leq b}f(x_j) = \sum_{a<x_j\leq b}p_j.$

The definition can be extended to continuous random variables in a direct way: The probability of $ x$ being on a given interval is calculated with an integral instead of using a summation:

$\displaystyle P[a<X\leq b] = \int_a^b f(x) dx.$

For a more formal approach using measure theory, look at probability distribution function entry.



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"density function" is owned by drini. [ full author list (2) | owner history (1) ]
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See Also: distribution function, cumulative distribution function, random variable, probability distribution function, geometric distribution

Other names:  probability function, density, probabilities function
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Cross-references: probability distribution function, theory, measure, summation, integral, continuous random variables, interval, calculate, random variable, function, discrete random variable
There are 26 references to this entry.

This is version 9 of density function, born on 2002-09-11, modified 2005-02-21.
Object id is 3450, canonical name is DensityFunction.
Accessed 20739 times total.

Classification:
AMS MSC60E05 (Probability theory and stochastic processes :: Distribution theory :: Distributions: general theory)

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