Probability density function problem

In summary, the conversation discusses the relationship between the PDF of something between two different bases or wavefunctions and the lowering operator in quantum mechanics. It is established that the lowering operator must produce an eigenfunction of the Hamiltonian with a different eigenvalue, and therefore all functions produced by the operator are orthogonal. This is supported by a general theorem stating that all eigenfunctions of a Hermitian operator with different eigenvalues are mutually orthogonal. The speaker confirms their understanding of the concept.
  • #1
Chronos000
80
0

Homework Statement



Is the PDF of something between two different bases or wavefunctions always 0?

For instance, if you have the lowering operator [tex]\hat{}a[/tex] -

<n|[tex]\hat{}a[/tex]|n>

that changes to <n|[tex]\sqrt{}n[/tex]|n-1> =0

I'm not sure I understand the physical scenario if this is true however.
 
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  • #2
Does the lowering operator produce a function orthogonal to the original? If it's guaranteed to produce an eigenfunction of the Hamiltonian with a different eigenvalue (it should, or else it's not a very useful lowering operator), then all functions produced by the operator have to be orthogonal. This is due to a more general theorem which says that all eigenfunctions of a Hermitian operator with different eigenvalues are mutually orthogonal.
 
  • #3
ok, I think I get this thanks
 

1. What is a probability density function (PDF)?

A probability density function (PDF) is a mathematical function that describes the probability of a random variable taking on a specific value or range of values. It is used to represent the distribution of a continuous random variable.

2. How is a PDF different from a probability mass function (PMF)?

A PDF is used to represent the probability distribution of a continuous random variable, while a PMF is used for discrete random variables. A PDF assigns probabilities to intervals of values, while a PMF assigns probabilities to individual values.

3. How do you calculate the area under a PDF curve?

The area under a PDF curve represents the probability of the random variable falling within a certain range of values. The area can be calculated by taking the integral of the PDF over the desired range of values.

4. Can a PDF have negative values?

No, a PDF cannot have negative values. The values of a PDF must always be greater than or equal to zero, as it represents the probability of a random variable occurring within a certain range of values.

5. What is the relationship between a PDF and a cumulative distribution function (CDF)?

A CDF is the cumulative sum of the PDF, and represents the probability of the random variable being less than or equal to a certain value. The PDF is the derivative of the CDF, and can be used to calculate the probability of the random variable falling within a specific range of values.

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