Mode of a probability distribution

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SUMMARY

The discussion focuses on calculating the expectation value and mode of a luminosity probability distribution defined by the equation p(L)dL = A (L/i)^a exp(-L/i) dL/i, where A is a constant, a = -0.7, and i = 1.4e10 solar luminosity units. The expected value is determined using the integral ∫ L p(L)dL. However, finding the mode is challenging due to the absence of a peak in the distribution, unlike Gaussian distributions, making traditional differentiation methods ineffective.

PREREQUISITES
  • Understanding of probability distributions, specifically luminosity probability distributions.
  • Familiarity with integration techniques in calculus.
  • Knowledge of exponential functions and their properties.
  • Basic concepts of astronomy related to luminosity measurements.
NEXT STEPS
  • Research methods for analyzing non-Gaussian probability distributions.
  • Learn about numerical integration techniques for expectation value calculations.
  • Explore advanced topics in astronomy related to luminosity and its statistical properties.
  • Investigate software tools for plotting probability distributions, such as Python's Matplotlib or R's ggplot2.
USEFUL FOR

Astronomers, statisticians, and students in physics or mathematics who are working with probability distributions and luminosity measurements will benefit from this discussion.

indie452
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Homework Statement



I have a luminosity prob distribution that i want to plot and find the expectation value and mode for.

Homework Equations



p(L)dL = A \frac{L}{i}^aexp(-\frac{L}{i}) \frac{dL}{i}

A=const
a= -0.7
i= 1.4e10 solar luminosity units
lower limit = 1e9 solar luminosity units

The Attempt at a Solution



I know that i can pick an arbitrarily large upper limit because as L increases much more than i, the exp term grows faster and the function goes to zero.

the expected value is just
\int L p(L)dL

but the mode is harder as usually we can differentiate the prob distribution and set it to zero to find turning point but this function plotted doesn't have a peak like on gaussian like plots
 
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