I What is Weighted Histogram Analysis and How Does it Work?

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Weighted Histogram Analysis Method (WHAM) involves applying weights to data points to reflect their importance in statistical analysis. A weight is essentially a factor that modifies the contribution of each data point, often calculated based on its relevance or frequency. The discussion highlights the difference between traditional probability density functions and weighted averages, emphasizing how weights influence the analysis outcome. Resources like umbrella sampling and specific methodologies are referenced to clarify the application of WHAM in various contexts, particularly in chemistry. Understanding weights and their calculation is crucial for effectively utilizing WHAM in data analysis.
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I would like to understand the 'weighted histogram analysis method' (wham), which is far to go, but before that am not sure what a weight is? {{Though I understand what is probability density function which has been already discussed on this forum here, and I assume applying weights is a different thing }}

Could someone please give a simple example which can help me to understand what a 'weight' is, and how it is calculated (or known), and then how the calculated (or known) weight is used on the same data?
 
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Here is a non-mathematical definition of a weighted average (mean), which may be a good starting point, from wikipedia:
an average resulting from the multiplication of each component by a factor reflecting its importance
so there is an effect imparted by the person or software doing the analysis. Example:
http://www.mathwords.com/w/weighted_average.htm

Now you get what weighted in the sense of data means.So here is a discussion of WHAM methodology:
https://www.quora.com/How-does-the-Weighted-Histogram-Analysis-Method-WHAM-work
 
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