probability density function

(noun)

Any function whose integral over a set gives the probability that a random variable has a value in that set.

Related Terms

Examples of probability density function in the following topics:

  • Probability

    • Probability density function describes the relative likelihood, or probability, that a given variable will take on a value.
    • Here, we will learn what probability distribution function is and how it functions with regard to integration.
    • In probability theory, a probability density function (pdf), or density of a continuous random variable, is a function that describes the relative likelihood for this random variable to take on a given value.
    • The probability density function is nonnegative everywhere, and its integral over the entire space is equal to one.
    • A probability density function is most commonly associated with absolutely continuous univariate distributions.

  • Continuous Probability Distributions

    • A continuous probability distribution is a probability distribution that has a probability density function.
    • In theory, a probability density function is a function that describes the relative likelihood for a random variable to take on a given value.
    • Unlike a probability, a probability density function can take on values greater than one.
    • The standard normal distribution has probability density function:
    • Boxplot and probability density function of a normal distribution $$$N(0, 2)$.

  • The Density Scale

    • Density estimation is the construction of an estimate based on observed data of an unobservable, underlying probability density function.
    • Density estimation is the construction of an estimate based on observed data of an unobservable, underlying probability density function.
    • A probability density function, or density of a continuous random variable, is a function that describes the relative likelihood for this random variable to take on a given value.
    • The above image depicts a probability density function graph against a box plot.
    • This image shows a boxplot and probability density function of a normal distribution.

  • Continuous Sampling Distributions

    • Moreover, in continuous distributions, the probability of obtaining any single value is zero.
    • Therefore, these values are called probability densities rather than probabilities.
    • A probability density function, or density of a continuous random variable, is a function that describes the relative likelihood for this random variable to take on a given value.
    • The probability for the random variable to fall within a particular region is given by the integral of this variable's density over the region .
    • Boxplot and probability density function of a normal distribution $N(0, 2)$.

  • From histograms to continuous distributions

    • This smooth curve represents a probability density function (also called a density or distribution), and such a curve is shown in Figure 2.28 overlaid on a histogram of the sample.
    • A density has a special property: the total area under the density's curve is 1.
    • Density for heights in the US adult population with the area between 180 and 185 cm shaded.

  • Two Types of Random Variables

    • The mathematical function describing the possible values of a random variable and their associated probabilities is known as a probability distribution.
    • Their probability distribution is given by a probability mass function which directly maps each value of the random variable to a probability.
    • The resulting probability distribution of the random variable can be described by a probability density, where the probability is found by taking the area under the curve.
    • The image shows the probability density function (pdf) of the normal distribution, also called Gaussian or "bell curve", the most important continuous random distribution.
    • This shows the probability mass function of a discrete probability distribution.

  • Properties of Continuous Probability Distributions

    • The curve is called the probability density function (abbreviated: pdf).
    • We use the symbol f (x) to represent the curve. f (x) is the function that corresponds to the graph; we use the density function f (x) to draw the graph of the probability distribution.
    • Area under the curve is given by a different function called the cumulative distribution function (abbreviated: cdf).
    • The cumulative distribution function is used to evaluate probability as area.
    • In general, calculus is needed to find the area under the curve for many probability density functions.

  • Continuous Probability Functions

    • We begin by defining a continuous probability density function.
    • We use the function notation f (x).
    • In the study of probability, the functions we study are special.
    • We define the function f (x) so that the area between it and the x-axis is equal to a probability.
    • This particular function, where we have restricted x so that the area between the function and the x-axis is 1, is an example of a continuous probability density function.

  • The Wave Function

    • A wave function is a probability amplitude in quantum mechanics that describes the quantum state of a particle and how it behaves.
    • In quantum mechanics, a wave function is a probability amplitude describing the quantum state of a particle and how it behaves.
    • Although ψ is a complex number, |ψ|2 is a real number and corresponds to the probability density of finding a particle in a given place at a given time, if the particle's position is measured.
    • If these requirements are not met, it's not possible to interpret the wave function as a probability amplitude.
    • Relate the wave function with the probability density of finding a particle, commenting on the constraints the wave function must satisfy for this to make sense

  • Multiple Regression Models

    • Multiple regression is used to find an equation that best predicts the $Y$ variable as a linear function of the multiple $X$ variables.
    • The purpose of a multiple regression is to find an equation that best predicts the $Y$ variable as a linear function of the $X$ variables.
    • Multiple regression would give you an equation that would relate the tiger beetle density to a function of all the other variables.
    • For example, if you did a regression of tiger beetle density on sand particle size by itself, you would probably see a significant relationship.
    • If you did a regression of tiger beetle density on wave exposure by itself, you would probably see a significant relationship.