What Function Produces a Smooth Curve with Specific Symmetry and Decay?

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SUMMARY

The discussion focuses on identifying mathematical functions that produce smooth curves with specific symmetry and decay characteristics. The lognormal probability density function (PDF) is highlighted as a suitable candidate, characterized by two adjustable parameters, μ and σ, which modify its shape. Additionally, the function y = x^n e^(-cx) is proposed, where the peak occurs at x = n/c, and it approaches zero as x approaches infinity. GeoGebra is recommended as a tool for visualizing these functions.

PREREQUISITES
  • Understanding of probability distributions, specifically lognormal and Weibull distributions.
  • Familiarity with mathematical functions and their parameters, such as μ and σ.
  • Basic knowledge of calculus, particularly exponential functions and their properties.
  • Experience with GeoGebra for plotting mathematical functions.
NEXT STEPS
  • Learn how to manipulate the lognormal PDF parameters in GeoGebra.
  • Explore the properties and applications of Weibull and Pareto distributions.
  • Study the implications of the parameters in the function y = x^n e^(-cx).
  • Investigate the relationship between the mean and variance in lognormal distributions.
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Mathematicians, physicists, data analysts, and anyone interested in statistical modeling and curve fitting using probability distributions.

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TL;DR
Is there a simple function that would produce a curve in this family?
Helping someone with some fictional physics.

He's looking for a function that will produce a curve similar to this (poor geometry is my doing, assume smooth curvature):
1630359239267.png

Starts at 0,0.
Maximum at n.
Reaches zero at infinity.
The cusp is not sharp, it's a curve (which, I think suggests at least two variables?).

Presumably, the curve is symmetrical about n logarithmically, but not a given.
 
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The lognormal pdf curve matches the four criteria listed, as do a number of other right-skewed, long-tailed probability distributions (could try pareto, weibull).

It has two parameters ##\mu,\sigma## that can be adjusted to modify the shape.

To make the maximum (mode of the distribution) happen at ##x=n## we set a constraint:

$$n=\mu-\sigma^2$$
 
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andrewkirk said:
The lognormal pdf curve matches the four criteria listed, as do a number of other right-skewed, long-tailed probability distributions (could try pareto, weibull).

It has two parameters that can be adjusted to modify the shape.
Heh. I literally just stumbled upon this before tabbing back here.
 
##x^n e^{-cx}##
It starts at 0,0, it has a maximum at x=n/c, it goes to zero for x to infinity.

If the logarithmic property is required, ##e^{-\log(x/n)^2}## will do the job (it's just a normal distribution adjusted using log(x)), but it's a bit more complicated.
 
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mfb said:
##x^n e^{-cx}##
It starts at 0,0, it has a maximum at x=n/c, it goes to zero for x to infinity.

If the logarithmic property is required, ##e^{-\log(x/n)^2}## will do the job (it's just a normal distribution adjusted using log(x)), but it's a bit more complicated.
Thanks. What is c?

Also,where can I plug this into see it?
 
DaveC426913 said:
Thanks. What is c?
You should start with the fact that you want the peak to be at n. THIS IS NOT NECESSARILY THE n IN NBF'S POST. Using ##y= x^m e^{-cx}##, the peak is at ##m/c=n## (your ##n##). So you have a new function, ##y= x^{nc} e^{-cx}## with only the parameter, ##c##.
DaveC426913 said:
Also,where can I plug this into see it?
I like the free download of GeoGebra. It is easy to type in an equation and get a plot. In this case, you would want to first set up a parameter, c, and set the integer, n, to a value. Then define the function ##y= x^{nc} e^{-cx}##.
Here is an example.
1630391481588.png
 
Last edited:
DaveC426913 said:
Thanks. What is c?
An adjustable parameter. The ratio n/c is the position of the peak.

Example plots, both with their peak at x=1. I multiplied the second example by 3 for better comparison.
 
Here is a GeoGebra example of the lognormal method. Suppose you want the maximum value to be at n=2. That is the ##mean## of the lognormal distribution. So ##mean=2##. The lognormal has two parameters: ##NormMean## and ##NormVariance##. You can set the ##NormVariance \gt 0## parameter as you wish. The smaller you make it, the higher the maximum at n=2. The value of ##NormMean## is calculated as ##NormMean = ln(mode)+NormVariance^2##. With these parameters set, the lognormal PDF is determined. Here is the GeoGebra example.
1630425031073.png
 
andrewkirk said:
To make the maximum (mode of the distribution) happen at ##x=n## we set a constraint:

$$n=\mu-\sigma^2$$
I think that should be ##n=e^{\mu-\sigma^2}##
 
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Thanks all.
 
  • #11
What you have drawn looks like a Poisson distribution to me ...
 
  • #12
Svein said:
What you have drawn looks like a Poisson distribution to me ...
No.

This, sir, is a Poisson Distribution:
1630532463267.png


*for clarity that's a moustache, beret and ascot on a cod.
 
Last edited:

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