The real meaning of probability density?

In summary: And a probability amplitude is (I think, maybe not always) a complex-valued function that has the property that the integral of its norm over all space is one. The famous Shroedinger Wave Equation is like this.
  • #1
jeebs
325
4
In QM people always talk about probability amplitudes and probability densities, but I've never really given these terms much thought and have always just ignored the density/amplitude part and focussed on the probability part.

So what is meant by a probability density - is it what it sounds like, ie. a probability per unit volume, or something different? And how is this distinct from a probability amplitude?
 
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  • #3
jeebs said:
In QM people always talk about probability amplitudes and probability densities, but I've never really given these terms much thought and have always just ignored the density/amplitude part and focussed on the probability part.

So what is meant by a probability density - is it what it sounds like, ie. a probability per unit volume, or something different? And how is this distinct from a probability amplitude?

A probability density function is any function that is everywhere non-negative and integrates over all space to the number one. The basic problem is that when you have infinitely many possibilities and the probability of each possibility is zero. So you have a probability density function and to calculate a probability you integrate over a positive interval.

A probability amplitude is (I think, maybe not always) a complex-valued function that has the property that the integral of its norm over all space is one. The famous Shroedinger Wave Equation is like this.
 
  • #4
PatrickPowers said:
A probability density function is any function that is everywhere non-negative...

everywhere... where?
 
  • #5
jfy4 said:
everywhere... where?


The pdf is nonnegative over the domain on which it is defined. This is just a fancy way of saying that a pdf cannot have a negative value anywhere over its domain.
 

FAQ: The real meaning of probability density?

What is the definition of probability density?

Probability density is a concept in statistics and probability theory that is used to describe the likelihood of a random variable taking on a certain value within a given range. It is often represented as a probability distribution function and is used to calculate the probability of a continuous variable falling within a specific interval.

How is probability density different from probability?

While probability is a measure of the likelihood of an event occurring, probability density is a measure of the likelihood of a continuous random variable taking on a specific value within a range. Probability is typically used for discrete events, while probability density is used for continuous variables.

What is the importance of probability density in statistics?

Probability density is an important concept in statistics as it allows us to describe the distribution of a continuous random variable. This can help us understand the behavior of the variable and make predictions about future outcomes. It also allows us to calculate the probability of a variable falling within a specific range, which is useful in many real-world applications.

How is probability density calculated?

Probability density is calculated by taking the derivative of the cumulative distribution function (CDF) of a continuous variable. The CDF represents the probability of a variable falling below a certain value, while the derivative represents the rate of change of this probability. This results in a probability density function, which can then be used to calculate probabilities for specific values or ranges.

What are some common misconceptions about probability density?

One common misconception about probability density is that it represents the probability of a specific value occurring. In reality, probability density represents the likelihood of a value falling within a certain range. Another misconception is that probability density can exceed 1, when in fact it is a relative measure and can only be compared to other probability densities within the same distribution.

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