Understanding a boundary condition on the density of probability

In summary, the book discusses the probability density ##P(x|y,t)##, which represents the likelihood of the potential having a value x at time t, given that it had a value y at t=0. The author notes that this notation may be confusing and suggests using ##P(x,t|y,0)## instead. The book then introduces a system with an absorbing potential B and a reflective potential -infinity, with boundary conditions that include ##P(B|y,t)=0##. The author's understanding of this condition is that the probability density of the potential having a value of B at time t, given that it had a value y at t=0, is 0. However, the book states that this
  • #1
fluidistic
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The book states that ##P(x|y,t)## represents the probability density that the potential has a value x at time t, knowing that it had the value y at t=0.
I understand this, the words are very clear. However I'd find much more intuitive the notation ##P(x,t|y,0)##, but I guess that's just me?

Now comes the problem:
Then the book states that for a particular system where B is an absorbing potential and -infinity is a reflective potential, it states that the boundary conditions are:
(1)##P(x|y,0)=\delta (y-x)##
(2)##P(B|y,t)=0##
(3)##P(-\infty |y,t)=0##
Here is my understanding of (2): Using my own notation I rewrite it as ##P(B,t|y,0)=0## which means that the density of probability that the potential has a value of B at time t, knowing that it had a value of y at time t=0 is 0.
However the book states that (2) says: The second condition reflects the existence of a threshold potential B such that when x(t) takes the value B, the process is absorbed.
For the (1), my understanding is : The probability density that the potential has a value of x at time t=0 knowing that it had the value y at time t=0 is worth ##\delta (y-x)##. Which makes perfect sense to me if this is the Dirac delta. However the book states that (1) : no change can occur in a zero time interval. A weird statement to me, but after all it's true.
So I understand this part and also the 3rd condition.
So I'm stuck at understanding the second condition.
Does this mean that the probability of finding the potential to be very close to B is approximately null, because it is "an absorbing potential"?
 
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  • #2
fluidistic said:
The book states that ##P(x|y,t)## represents the probability density that the potential has a value x at time t, knowing that it had the value y at t=0.
I understand this, the words are very clear. However I'd find much more intuitive the notation ##P(x,t|y,0)##, but I guess that's just me?

Now comes the problem:
Then the book states that for a particular system where B is an absorbing potential and -infinity is a reflective potential, it states that the boundary conditions are:
(1)##P(x|y,0)=\delta (y-x)##
(2)##P(B|y,t)=0##
(3)##P(-\infty |y,t)=0##
Here is my understanding of (2): Using my own notation I rewrite it as ##P(B,t|y,0)=0## which means that the density of probability that the potential has a value of B at time t, knowing that it had a value of y at time t=0 is 0.
This is exactly backwards. Saying that B is "absorbing" means the probability the potential has value y at time t, knowing it had value B at time t= 0, is 0. The "object" has been "absorbed" and can never have any other value in the future.

However the book states that (2) says: The second condition reflects the existence of a threshold potential B such that when x(t) takes the value B, the process is absorbed.
For the (1), my understanding is : The probability density that the potential has a value of x at time t=0 knowing that it had the value y at time t=0 is worth ##\delta (y-x)##. Which makes perfect sense to me if this is the Dirac delta. However the book states that (1) : no change can occur in a zero time interval. A weird statement to me, but after all it's true.
So I understand this part and also the 3rd condition.
So I'm stuck at understanding the second condition.
Does this mean that the probability of finding the potential to be very close to B is approximately null, because it is "an absorbing potential"?
 
  • #3
HallsofIvy said:
This is exactly backwards. Saying that B is "absorbing" means the probability the potential has value y at time t, knowing it had value B at time t= 0, is 0. The "object" has been "absorbed" and can never have any other value in the future.

So if I understand you well, mathematically this would translate as ##P(y,t|B,0)=0## using my own notation or ##P(y|B,t)=0## using the book notation.
So in both cases the book is wrong by writting ##P(B|y,t)=0##?
 

What is a boundary condition on the density of probability?

A boundary condition on the density of probability is a mathematical constraint that must be satisfied in order for a probability distribution to be valid. It defines the behavior of the probability distribution at the boundaries of its domain.

Why is understanding a boundary condition on the density of probability important?

Understanding a boundary condition on the density of probability is important because it allows us to accurately interpret and analyze the behavior of a probability distribution. It ensures that the distribution is well-defined and can be used for making predictions or modeling real-world phenomena.

What are some common examples of boundary conditions on the density of probability?

Some common examples of boundary conditions on the density of probability include the requirement that the total probability of all outcomes must equal 1, or that the probability of a certain event must be greater than or equal to 0. These conditions are often used in the context of binomial or normal distributions.

How do boundary conditions affect the shape of a probability distribution?

Boundary conditions can greatly affect the shape of a probability distribution. For example, if the boundary condition is that the distribution must be symmetric, this will restrict the possible shapes of the distribution and may result in a more familiar and predictable curve.

What happens if a boundary condition is not satisfied?

If a boundary condition is not satisfied, the probability distribution will not accurately represent the phenomenon it is meant to model. This can lead to incorrect predictions or unreliable results. In some cases, the distribution may be completely invalid and unusable. Therefore, it is important to carefully consider and understand the boundary conditions when working with probability distributions.

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