Guassian Probability density function

In summary, the PDF of a Gaussian variable x is given by $$p_x(x)=\frac{1}{C \sqrt{2 \pi}} e^{\frac{-(x-4)^2}{18}}$$ and the standard deviation C can be found by substituting the given values into the formula. To find the probability of x≥2, we can use the Q(x) function and look up the result in the corresponding table.
  • #1
Evo8
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Homework Statement



The PDF (probability density function) of a Gaussian variable x is given by.

$$p_x(x)=\frac{1}{C \sqrt{2 \pi}} e^{\frac{-(x-4)^2}{18}}$$

a) Find C
b)find the probability of x≥2 --> ##P(x≥2)##

Homework Equations



$$ \frac{dF_X(x)}{dx} x=P(x<X≤x+Δx)$$

The Attempt at a Solution



So i get stuck on how to solve the above for C. I have an example of a similar problem that my professor did in class but it skips a lot of steps that I need to see to fully understand. It seems like he started with taking the integral of the signal by using an integral table?

In my textbook I do see that the above is a standard of a gaussian or normal probability density. It looks something like this.

$$p_X(x)=\frac{1}{\sqrt{2 \pi}} e^{-x^2}{2}$$
$$F_X(x)=\frac{1}{\sqrt{2 \pi}} \int_{-\infty}^{x} e^{\frac{-x^2}{2}}dx$$

Any hints on where to start?

Any help is much appreciated! Thank you!
 
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  • #2
The parameter C is the standard deviation.
The denominator of the power of the exponent is equal to 2(C^2).
Hence C = 3, as 2 times 9 is 18.

[tex]p_x(x)=\frac{1}{3 \sqrt{2\pi}}e^{\frac{-(x-4)^2}{18}}[/tex]

My approach to this was to look at the formula given on this wikipedia page:
https://en.wikipedia.org/wiki/Normal_distribution

To find the probability of x=2 I think maybe we could substitute.

[tex]p_x(2)=\frac{1}{3 \sqrt{2\pi}}e^{\frac{-(2-4)^2}{18}}[/tex]
 
Last edited:
  • #3
[tex]x^2\sqrt{x}[\tex]
was trying some latex here. semi-success.
 
  • #4
Substituted the value for x=2 in my Casio and I get 0.1064826685.
Will try and plot in Mathematica for confirmation.
 
  • #5
Have plotted them :)

The files are attached to this post. Need to work out how to get them to flash up here.
 

Attachments

  • GaussianPhysForum.nb
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  • GaussianPhysForum.cdf
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  • #6
Plotted :)

Not sure quite how to embed the image so that it appears in this post. But its attached & the hand-calculated value looks reasonable :)

GaussianPhysForum.gif


GaussianPhysForumzoom.gif


yes! Think this is how its done.
 
  • #7
Wow it was really that simple! I had that equation written down right on the scratch pad where I was working this problem and didnt see that I guess.

To find the probability I followed that other example that simply used the ##Q(x)## function. And then take the result and look up the probability in the table that goes along with that function.

For reference the function looks like this (from my text) ## Q(x)= \frac{1}{x \sqrt{2 \pi}} e^{\frac{-x^2}{2}}##

Thanks for the help AugustCrawl!
 
  • #8
Oh btw a little note. Your latex code looks ok. If you use those tage be sure to use "[\itex]" i think your just leaving out the i. Or you can use two dollar signs $$ before and after for a separate line of code or two hash tags ## for code to be on the same line.
 

Related to Guassian Probability density function

What is a Gaussian probability density function?

A Gaussian probability density function is a mathematical function that is used to describe the probability distribution of a continuous random variable. It is also known as the normal distribution and is commonly used in statistics and probability theory.

What are the characteristics of a Gaussian probability density function?

There are three main characteristics of a Gaussian probability density function: it is bell-shaped, symmetric around the mean, and its mean, median, and mode are all equal. It also follows the 68-95-99.7 rule, which states that approximately 68% of the values fall within 1 standard deviation of the mean, 95% within 2 standard deviations, and 99.7% within 3 standard deviations.

How is a Gaussian probability density function calculated?

A Gaussian probability density function is calculated using the formula: f(x) = (1/(σ√2π)) * e^(-(x-μ)^2/(2σ^2)), where μ is the mean and σ is the standard deviation of the distribution. This formula can also be simplified using a z-score, which is the number of standard deviations a value is from the mean.

What is the significance of the Gaussian probability density function?

The Gaussian probability density function has significant importance in statistics and probability theory. It is used to describe many natural phenomena, such as height and weight distributions, and is also used in various statistical tests and models. It is also a fundamental concept in the central limit theorem, which states that the sum of a large number of independent random variables will follow a normal distribution.

How is the Gaussian probability density function different from other probability distributions?

The Gaussian probability density function is unique because of its bell-shaped curve and symmetrical nature. It is also a continuous distribution, meaning that the possible values of the random variable can take on any value within a certain range. Other probability distributions, such as the binomial and Poisson distributions, are discrete and have different shapes and characteristics.

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