Understanding Probability Density Equation & Example

In summary, the conversation is about the probability density equation, which involves the position-space wavefunction, \Psi, and its gradient. The equation also includes constants such as \hbar, m, and i. There is confusion about whether \Psi is a ket on the right side of the equation, with the conclusion being that it is not a ket but rather a position-space wavefunction. A concrete example is given of what can be plugged into the expression, using the 1D infinite square well as an example. It is also mentioned that the gradient of a scalar field is a vector field.
  • #1
ehrenfest
2,020
1
I am confused about the the probability density equation:

[tex] \vec j = \frac{\hbar}{2mi}\left(\Psi^* \vec \nabla \Psi - \Psi \vec \nabla \Psi^*\right)[/tex]

Psi is not a ket on the right side, correct?
If not how can perform a del on it and get a vector on the right side?

Can someone give me a concret example of what you would plug into this expression:
[tex]\Psi^* \vec \nabla \Psi [/tex]
 
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  • #2
Psi is not a ket, it is a position-space wavefunction, given by the inner product: [itex]\psi _n(x) = \langle x|n \rangle [/itex]

E.g., in the 1D infinite square well of width L, [itex]\psi_n(x)=\sqrt{2/L}~sin(n \pi x/L) [/itex]

The gradient of a scalar field is a vector field.
 

FAQ: Understanding Probability Density Equation & Example

What is the Probability Density Equation?

The Probability Density Equation, also known as the probability density function, is a mathematical expression that describes the likelihood of a random variable taking on a particular value within a given range. It is denoted by the symbol "f(x)" and is often used to calculate the probability of an event occurring.

How is the Probability Density Equation different from the Probability Mass Function?

The Probability Density Equation is used for continuous random variables, while the Probability Mass Function is used for discrete random variables. The main difference is that the Probability Density Equation assigns probabilities to ranges of values, while the Probability Mass Function assigns probabilities to specific values.

Can you provide an example of the Probability Density Equation?

Sure, let's say we have a continuous random variable representing the weight of apples in a basket. The Probability Density Equation for this variable could be f(x) = 1/2.5e^(-x/2.5), where x is the weight of the apple in pounds. This equation tells us that the probability of an apple weighing exactly 2 pounds is 0, but the probability of an apple weighing between 1.5 and 2.5 pounds is 1/2.5 or 0.4.

How is the Probability Density Equation used in real life?

The Probability Density Equation is used in a variety of fields, including finance, engineering, and physics. It is often used to model and predict the behavior of complex systems, such as stock prices, traffic flow, and particle movement. It is also used in statistical analysis to make inferences and draw conclusions from data.

What is the relationship between the Probability Density Equation and the Normal Distribution?

The Normal Distribution, also known as the Gaussian Distribution, is a probability distribution that is commonly used in statistics and probability theory. It is characterized by its bell-shaped curve and is often used to model natural phenomena. The Probability Density Equation for the Normal Distribution is f(x) = 1/(σ√(2π))e^(-(x-μ)^2/(2σ^2)), where μ is the mean and σ is the standard deviation. This equation is used to calculate the probabilities of different values occurring within the Normal Distribution curve.

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