Likelihood of some points fitting a derived function....

In summary, the conversation discusses the need to plot the total mass of points in each shell versus the radius of the shell, using a fitting function and calculating the associated likelihood. However, there is uncertainty about what exactly is meant by "likelihood" and what the appropriate error term (sigma) should be. The person providing advice suggests stating a mathematical model and assumptions for the data before proceeding with further steps.
  • #1
Silviu
624
11
Hello! I have some concentrical spheres with many points inside. And I need to plot the total mass of points in each shell (so between 2 spheres) versus the radius of that shell (defined as (r1+r2)/2, where r1 and r2 are the radius of the 2 spheres forming the shell). I have 10 shells so my plot has 10 points and I want to fit them with a certain function and I need to calculate the likelihood associated with this fitting function. But for this, I need a sigma and I don't know what exactly would that be. So i know the mass, radius and number of points in each shell. I thought to take the error something like sqrt(number of particles) but I am not sure. What should I do to calculate the likelihood?

Thank you!
 
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  • #2
Silviu said:
I need to calculate the likelihood associated with this fitting function.

What is the probability model for the situation ?

Unless you can state a mathematical model, you'll only get vague and general advice.

"Liklihood" is technical term in statistics, and I don't know whether you intend to use it in a technical sense. It isn't clear what random variable you are talking about when you speak of "sigma". If the data in each shell follows a different probability distribution, the data in different shells might be realization of different random variables, each with its own standard deviation.
 
  • #3
Hey Silviu.

You should provide the model you want to use to fit the data and then provide the assumptions for the original data you are using.

After that, it's a matter of going through the steps [either using things like Central Limit Theorem if you have lots of data or using specific techniques if this is isn't the case].
 

Related to Likelihood of some points fitting a derived function....

1. What is the likelihood of some points fitting a derived function?

The likelihood of some points fitting a derived function depends on various factors such as the complexity of the function, the distribution of the data points, and the amount of noise in the data. It is also influenced by the chosen algorithm or method used to fit the function to the data.

2. How do you determine the likelihood of points fitting a derived function?

There are several methods for determining the likelihood of points fitting a derived function. These include statistical techniques such as regression analysis, maximum likelihood estimation, and Bayesian inference. Each method has its own strengths and limitations, so it is important to carefully consider the data and the underlying assumptions before choosing a method.

3. Can the likelihood of points fitting a derived function be accurately predicted?

The accuracy of predicting the likelihood of points fitting a derived function depends on the quality and quantity of data available, as well as the chosen method for fitting the function. In some cases, the likelihood can be accurately predicted, while in others, it may be difficult to determine due to the complexity of the data or the limitations of the chosen method.

4. How does the likelihood of points fitting a derived function affect the overall accuracy of the function?

The likelihood of points fitting a derived function is an important factor in determining the overall accuracy of the function. A high likelihood indicates a strong correlation between the data points and the derived function, resulting in a more accurate fit. On the other hand, a low likelihood may indicate that the derived function does not accurately represent the data.

5. Can the likelihood of points fitting a derived function change over time?

Yes, the likelihood of points fitting a derived function can change over time. This can be due to changes in the data, changes in the underlying relationships between the variables, or changes in the chosen method for fitting the function. It is important to regularly reassess the likelihood and adjust the derived function if necessary to ensure accurate predictions.

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