Understanding Probability Density Functions for Beginners

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In summary, the conversation discusses probability density functions and their relationship to the area under the curve. The units of a pdf are "probability per unit length" and it is a dimensionless quantity in physics. The conversation also mentions the use of a histogram to illustrate this concept and the role of a sample space in defining probability. Finally, the conversation brings up the use of a histogram to define a pdf and the lack of information about the variables involved.
  • #1
member 428835
hello all!

i am wondering about probability density functions. i know the area under a pdf gives the probability of an event, but i am having a difficult time seeing this. specifically, given a pdf we have ##\int_a^b f(x) dx## as the probability of an occurrence from ##[a,b]##. what are the units of ##f(x)##? why exactly is ##f \times dx## the probability, rather than just ##f## itself?

please illustrate with a histogram if you can. thanks!
 
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  • #2
i should add, in the text I'm using we are given that, if ##H(c, \Delta c, N)## where ##\Delta c## is the slot width, ##N## is the number of realizations of the random variable. evidently $$B(c) := \lim_ {\substack{\Delta c \to 0 \\ N \to \infty}}\frac{H(c, \Delta c, N)}{\Delta c}$$

where ##B(c)## is a pdf. can someone help explain this? nothing was really said about ##c## or ##H## other than ##H## is the histogram. i assume ##c## is a random variable?
 
  • #3
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joshmccraney said:
what are the units of ##f(x)##? why exactly is ##f \times dx## the probability, rather than just ##f## itself?

Think of "density" in terms of physical density - for example, let f(x) be the mass density of a rod given in terms of kilograms per meter at the point x. To find the mass of a rod between two points, you'd do an integration. The value of f(x_0) at the point x_0 is some value of density, not a value indicating mass.

You could say that the "units" of a pdf are "probability per unit length" (or "per unit area" etc.) , but I don't know if anyone has ever worked out a good way for abbreviating all the information that goes along with "probability" in the same system we use for units in physics. To speak of "the probability" of an event unambiguously, you have to define a "sample space" and an algebra of sets of events and a measure defined on that algebra. If we say that a particular formula "is a pdf" then we convey a lot of mathematical conventions with that short phrase. As far as I know, in physics "probability" is a "dimensionless" quantity. From that point of view, the "units" of a pdf are 1 over the unit of measure used on the sample space. .
 
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  • #4
joshmccraney said:
we are given that, if ##H(c, \Delta c, N)##

You didn't say what [itex] H [/itex] is. You only defined its arguments.
 
  • #5
thanks for the reply! yea, tho author of the text on states that ##H## is a histogram. nothing more is stated that i haven't already listed...it's pretty annoying.
 

FAQ: Understanding Probability Density Functions for Beginners

What is a probability density function (PDF)?

A probability density function (PDF) is a mathematical function that describes the probability of a random variable falling within a certain range of values. It is often used to model continuous random variables, such as height or weight.

How is a PDF different from a probability distribution function (PDF)?

A probability distribution function (PDF) is a function that describes the probability of a discrete random variable taking on a specific value. A PDF, on the other hand, describes the probability of a continuous random variable falling within a range of values. While a PDF can take on any value between 0 and 1, a PDF can only take on values of 0 or 1.

What is the area under a PDF curve?

The area under a PDF curve is equal to 1. This means that the total probability of all possible outcomes for a given continuous random variable is equal to 1.

How do you interpret a PDF?

A PDF can be interpreted as the relative likelihood of a continuous random variable falling within a range of values. The higher the value of the PDF at a certain point, the more likely it is for the random variable to take on a value within that range.

What are some common types of PDFs?

Some common types of PDFs include the normal distribution, uniform distribution, exponential distribution, and beta distribution. These distributions are often used to model real-world phenomena and can be useful in making predictions and analyzing data.

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