Finding a partial differential to binomial distribution

In summary, the conversation discusses the different interpretations of the variable N and its derivative in a mathematical context. The participants discuss the potential meanings of N, including its representation as a probability distribution and its potential abstract nature. They also consider the application of the chain rule to determine the derivative of N.
  • #1
axiomlu
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  • #2
How many times are you going to post the same question? Well, maybe it is not the same! Previously, you said "binormial", which I had first interpreted as "binomial", which you have here, but apparently you meant "bi-normal", a normal distribution in two variables. I would expect "N" to be a normal distribution which has a single variable with the mean and standard deviation as
parameters. I don't know what you mean by "[tex]N(a, b, c, d, \rho)[/tex]", with 5 variables. Also, since N reduces to a function in the single variable, you should get [tex]\frac{dN}{dx}[/tex], not [tex]\frac{\partial N}{\partial x}[/tex] but that is a matter of notation, not substance.

In any case, by the "chain rule", [tex]\frac{dN}{dx}= \frac{\partial N}{\partial a}\frac{da}{dx}+ \frac{\partial N}{\partial b}\frac{db}{dx}+ \frac{\partial N}{\partial c}\frac{dc}{dx}+ \frac{\partial N}{\partial d}\frac{dd}{dx}+ \frac{\partial N}{\partial \rho}\frac{d\rho}{dx}[/tex].

Since a= 0.5x+ 3, da/dx= 0.5, b= -2x, db/dx= -2, [tex]c= x^2[/tex], dc/dx= 2x, d= x+ 0.2, dd/dx= 1, [tex]\rho= 0.4x- 0.2[/tex], [tex]d\rho/dx= 0.4[/tex] so
[tex]\frac{dN}{dx}= 0.5\frac{\partial N}{\partial a}- 2\frac{\partial N}{\partial b}+ 2x\frac{\partial N}{\partial c}+ \frac{\partial N}{\partial d}+ 0.4\frac{\partial N}{\partial \rho}[/tex].
 
  • #3
Hi axiomlu,

What does $N$ represent?
Normally it would represent a probability distribution, and more specifically the Normal Distribution.
However, that is not a function that we can take a derivative of.
And it is already clear that it is not the normal distribution, since that one has a single mean and a single variance.

Is it perhaps supposed to represent the probability density function of a distribution?
Or is it supposed to be something abstract. If so, HallsofIvy's response gives the best we can do.
 

1. What is a partial differential to binomial distribution?

A partial differential to binomial distribution is a mathematical equation that describes the probability of a certain number of successes in a fixed number of independent trials, where each trial has the same probability of success. It is often used in statistics and probability to model real-world scenarios.

2. How is a partial differential to binomial distribution different from a regular binomial distribution?

A partial differential to binomial distribution takes into account the continuous nature of the variables involved, while a regular binomial distribution only considers discrete variables. This means that a partial differential to binomial distribution can be used to model situations where the number of trials or the probability of success can vary continuously.

3. What are the key components of a partial differential to binomial distribution?

The key components of a partial differential to binomial distribution are the number of trials, the probability of success in each trial, and the number of successes. These variables are used to calculate the probability of obtaining a specific number of successes in a given number of trials.

4. How is a partial differential to binomial distribution calculated?

A partial differential to binomial distribution can be calculated using the formula P(x) = (n choose x) * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes, and p is the probability of success in each trial. This formula is used to calculate the probability of obtaining exactly x successes in n trials.

5. What are some real-world applications of a partial differential to binomial distribution?

A partial differential to binomial distribution can be used in various fields, such as finance, biology, and engineering. For example, it can be used to model the probability of a stock price reaching a certain level, the likelihood of a drug being effective in a clinical trial, or the chance of a machine failing during production. It is a versatile tool for analyzing and predicting outcomes in many different scenarios.

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