Should I Normalize My Discretized Integral?

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The discussion centers on the evaluation of a discretized integral using a joint histogram representation of the probability density function (PDF) P(v, f). Luca computes the integral as a sum over bins, specifically using the formula ∑_{r}^{bins} P(v,f)/P(f). The key insight provided by forum members is the necessity of including a normalization factor, Δx, in the Riemann sum approximation to accurately represent the integral. This normalization is crucial for ensuring the integrity of the computed value.

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pamparana
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Hello everyone,

I have an integral as follows:

[itex]\log(\int_{r}^{\inf}\frac{P(v,f)}{P(f)}dv)[/itex]

Now, I want to evaluate this on the computer where this PDF represented by P(v, f) is build up using a joint histogram where each entry is a probability for a given value pair for v and f to occur together.

Now when I compute this integral I do it as follows:
[itex]\sum_{r}^{bins}\frac{P(v,f)}{P(f)}[/itex]

where v and f are some appropriate values and bins are the number of bins along the appropriate axes in my joint histogram. Then after accumulation, I take the log of the final value.

Now, my question is that do I need to normalize this somehow? When discretizing such integrals does one normalize it by the range of the definite integral parameters?

Thanks,

Luca
 
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You should make a graphic. What you basically do is to approximate the integral by a Riemann sum. This involves the split of the distance into equidistant sections.
 
You're missing a term.

As @fresh_42 says, you appear to be doing a Riemann sum, approximating ##\int_a^b f(x) dx## by ##\sum_{i=1}^n f(x_i) \Delta x ## = ##\Delta x \sum_{i=1}^n f(x_i)## if all the bins are the same size. So you need the ##\Delta x##.
 

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