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

In summary, the person is looking for a function that produces a curve similar to this one. They have found that the lognormal PDF produces a curve that matches four criteria. The two parameters that can be adjusted to modify the shape are the ratio n/c and the value of c.
  • #1
DaveC426913
Gold Member
23,070
6,747
TL;DR Summary
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.
 
Physics news on Phys.org
  • #2
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:

$$n=\mu-\sigma^2$$
 
  • Like
Likes FactChecker
  • #3
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.
 
  • #4
##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.
 
  • Like
Likes FactChecker
  • #5
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?
 
  • #6
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:
  • #7
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.
 
  • #8
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
 
  • #9
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}##
 
  • #10
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:

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

What is a first order approximation for a curve?

A first order approximation for a curve is a simplified version of the curve that uses a linear function to approximate the curve's behavior. It is often used in mathematical models to make calculations easier.

How is a first order approximation calculated?

A first order approximation is calculated by finding the slope of the tangent line at a given point on the curve. This slope is then used to construct a linear function that approximates the curve at that point.

What are the limitations of a first order approximation?

A first order approximation is only accurate at the specific point where it is calculated. It does not take into account the curvature of the curve or any other factors that may affect the behavior of the curve.

How accurate is a first order approximation?

The accuracy of a first order approximation depends on how close the point of approximation is to the actual curve. The closer the point is, the more accurate the approximation will be. However, for points that are far from the actual curve, the approximation may be significantly less accurate.

Can a first order approximation be used for any type of curve?

No, a first order approximation is only suitable for curves that have a linear behavior. Curves that have more complex behaviors, such as exponential or logarithmic curves, would require higher order approximations to accurately model their behavior.

Similar threads

Replies
13
Views
2K
Replies
2
Views
6K
Replies
2
Views
4K
6
Replies
175
Views
22K
Replies
5
Views
2K
Replies
6
Views
4K
Replies
1
Views
3K
Replies
2
Views
2K
Back
Top