Histogram normalisation: a question about terminology

Let's consider the histogram and histcounts functions, as in these two cases:
num_bins = 30; % <-- note: I specify the "number of bins" and not the "bin width", which can be different from 1
% Case 1
histcounts(X,num_bins,'Normalization','probability');
histogram(X,'NumBins',num_bins,'Normalization','probability');
% Case 2
histcounts(X,num_bins,'Normalization','pdf');
histogram(X,'NumBins',num_bins,'Normalization','pdf');
Do I understand correctly that
  1. For Case 1, I get the "Relative Frequency Histogram" or an "empirical estimate of the Probability Mass Function"?
  2. For Case 2, - where I divide by the bin widths as well -, I get the an "empirical estimate of the Probability Density Function"?

Risposte (1)

Hi Sim,
Yeah you got it right. Your understanding for both cases is correct.

3 Commenti

Sim
Sim il 1 Ago 2023
Modificato: Sim il 1 Ago 2023
Thanks for your comment @Satwik Samayamantry! Before accepting your answer, do you know/have some reference supporting your answer?
See the description of the Normalization name-value argument on either of the histogram or histcounts documentation pages. It describes exactly what the Values property of the histogram or the first output of the histcounts function represent.

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Richiesto:

Sim
il 14 Lug 2023

Commentato:

Sim
il 1 Ago 2023

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