Computation about Gaussian and Dirac Delta Function

In summary, the conversation discusses the use of a Gaussian distribution and a Dirac Delta function in a mathematical model called Adpredictor. The discussion revolves around a specific equation involving the Dirac Delta function and the truncated normal distribution. However, there is some confusion about the notation and some discrepancies in the paper. The conversation ends with the hope that someone more knowledgeable can provide clarification.
  • #1
keliu
2
0
I have a Gaussian distribution about t, say, N(t; μ, σ), and a a Dirac Delta Function δ(t).

Then how can I compute: N(t; μ, σ) * δ(t > 0)

Any clues? Or recommender some materials for me to read?

Thanks!
 
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  • #2
I've never seen the notation δ(t > 0) for a Dirac delta function, what do you mean by this? Also, Dirac delta functions are not really functions (they are "distributions") and should not really appear outside of an integral, so I'm not sure what you're trying to compute...maybe you could specify what your motivations are for looking at this quantity?
 
  • #3
In fact I am reading a paper about Microsoft's Adpredictor model. And here is a web article about the model's derivative process.

In the Step 7 of the web article, the author says that :
p(t) = N(t; μ, σ2)δ(y = sign(t)) and when y = 1, p(t) = N(t; μ, σ2)δ(t > 0) and then t has a truncated normal distribution. I don't understand how he could do that how he finish the full step 7.

Because the blog does not have any contact information about the owner, so I came here.

Thanks!
 
  • #4
I...I can't make heads or tails of that at all, sorry. Hopefully someone better versed in this than me can help you.
 
  • #5
As far as I can tell from the paper, it seems like either they goofed or the rendering in the pdf file had a problem. I could be wrong, but it seems to me that the two "delta" equations on the fourth page should read:
[tex]
p(s|\mathbf{x,w}) = \delta(s - \mathbf{w^T x}).
[/tex]
and
[tex]
p(y|t)= \delta(y - sign(t)).
[/tex]
These interpretations seem consistent with the words that precede each equation. jason
 

1. What is the Gaussian function?

The Gaussian function, also known as the Gaussian distribution or normal distribution, is a mathematical function that is commonly used to describe the distribution of continuous variables. It is often seen as a bell-shaped curve on a graph and is characterized by its mean and standard deviation.

2. How is the Gaussian function used in computation?

The Gaussian function is used in computation to model and simulate real-world phenomena that are normally distributed, such as measurements of physical quantities or errors in data. It is also frequently used in statistics, machine learning, and signal processing algorithms.

3. What is the Dirac delta function?

The Dirac delta function, also known as the unit impulse function, is a mathematical function that represents an infinitely narrow and tall spike at the origin on a graph. It is often used to model instantaneous events or impulses in physical systems.

4. How is the Dirac delta function related to the Gaussian function?

The Dirac delta function is often used as a limiting case of the Gaussian function, where the standard deviation approaches zero but the area under the curve remains constant. This relationship is used in many mathematical and computational applications, such as Fourier transforms and convolution.

5. What are some practical applications of computing with Gaussian and Dirac delta functions?

Gaussian and Dirac delta functions have a wide range of practical applications in various fields, including physics, engineering, economics, and computer science. Some examples include image and signal processing, data analysis and modeling, control systems, and quantum mechanics.

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