Looking for additional material about limits and distributions

In summary, the conversation is about the delta dirac function δ(τ) and the strict definition of it using generalized functions and distributions. The book briefly mentions generalized limits and the use of sequences of functions. The conversation also includes a request for additional resources on the topic.
  • #1
mynick
34
0
I would like some help to find some additional info on generalized functions, generalized limits. My aim is to understand the strict definition of delta dirac δ(τ).If you could provide a concise tutorial focusing on δ(τ) not the entire theory...it would be of great help. I am not a math person,i know basic calculus.Also, if you know other online resources like videos,websites etc on this topic it would be of great help.

I am studying the delta dirac function δ(τ). My book talks about a strict, mathy definition of the delta function. It is using a generalized function formula. It says that δ(τ) is a special case of a bigger family of generalized functions also known as distributions. ∫ f(t)x(t) dt = Nf[x(t)], the integral is from - infinity to + infinity. Then the book briefly proceeds in a few lines and describes generalized limits. lim∫ f(t)x(t) dt =∫ f(t)x(t) dt , where lim is n->infinity. It proceeds and says that limit fn(t)=f(t), again lim is n-> infinity. I am sorry i do not know how to insert math formulas.
 
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  • #2
mynick said:
I would like some help to find some additional info on generalized functions, generalized limits. My aim is to understand the strict definition of delta dirac δ(τ).If you could provide a concise tutorial focusing on δ(τ) not the entire theory...it would be of great help. I am not a math person,i know basic calculus.Also, if you know other online resources like videos,websites etc on this topic it would be of great help.
Have you looked here? https://en.wikipedia.org/wiki/Dirac_delta_function
This page describes the Dirac delta function as "a function that is equal to zero everywhere except for zero and whose integral over the entire real line is equal to one. As there is no function that has these properties, the computations that were done by the theoretical physicists appeared to mathematicians as nonsense, until the introduction of distributions by Laurent Schwartz..."
There are links on that page to distributions and generalized functions.
mynick said:
I am studying the delta dirac function δ(τ). My book talks about a strict, mathy definition of the delta function. It is using a generalized function formula. It says that δ(τ) is a special case of a bigger family of generalized functions also known as distributions. ∫ f(t)x(t) dt = Nf[x(t)], the integral is from - infinity to + infinity. Then the book briefly proceeds in a few lines and describes generalized limits. lim∫ f(t)x(t) dt =∫ f(t)x(t) dt , where lim is n->infinity.
Most likely in your formula above, you mean $$\lim_{n \to \infty} \int_{-\infty}^\infty f_n(t)x(t)dt = \int_{-\infty}^\infty f(t)x(t)dt$$
IOW the integral on the left involves a sequence of functions ##\{f_n(t)\}##.

mynick said:
It proceeds and says that limit fn(t)=f(t), again lim is n-> infinity. I am sorry i do not know how to insert math formulas.
See our tutorial here: https://www.physicsforums.com/help/latexhelp/
The script I used for the formula above looks like this: \lim_{n \to \infty} \int_{-\infty}^\infty f_n(t)x(t)dt = \int_{-\infty}^\infty f(t)x(t)dt
 

1. What are limits and distributions in statistics?

Limits and distributions refer to the concepts used to describe the behavior of a set of data. Limits are used to describe the behavior of a function as its input approaches a certain value. Distributions, on the other hand, describe the probability of different values occurring within a dataset.

2. Why are limits and distributions important in statistics?

Limits and distributions are important because they allow us to understand the behavior of data and make predictions based on that behavior. They also help us to identify patterns and trends within a dataset, which can be useful for making informed decisions.

3. What are common types of distributions in statistics?

Some common types of distributions in statistics include normal distribution, binomial distribution, and Poisson distribution. Normal distribution, also known as the bell curve, is often used to describe continuous data. Binomial distribution is used for binary outcomes, while Poisson distribution is used for count data.

4. How are limits and distributions used in hypothesis testing?

Limits and distributions are used in hypothesis testing to determine the probability of obtaining a certain result by chance. This is done by comparing the observed data to a known distribution and calculating the likelihood of obtaining that result. If the probability is low, it suggests that the observed result is not due to chance and may be statistically significant.

5. Are there any limitations to using limits and distributions in statistics?

While limits and distributions are useful tools in statistics, they do have some limitations. One limitation is that they assume the data is normally distributed, which may not always be the case. Additionally, outliers or extreme values can greatly affect the results of a distribution analysis. It is important to carefully consider the assumptions and limitations when using these concepts in statistical analysis.

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