Estimate the mean number of decays

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SUMMARY

The discussion focuses on estimating the mean number of decays from a dataset of approximately 3000 measurements of radioactive decay, utilizing the Poisson distribution. Participants agree that the arithmetic mean is a viable estimator, but question its superiority over fitting a Poisson distribution and deriving the mean from the fit coefficients. The conversation highlights the importance of considering the practical implications of estimation errors in real-world applications, particularly in radioactive waste management. A Bayesian perspective is also introduced, emphasizing the assumptions about the a priori distribution of rates.

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  • Understanding of Poisson distribution
  • Knowledge of statistical estimation techniques
  • Familiarity with Bayesian statistics
  • Experience in radioactive decay measurement
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  • Research the properties of Poisson distribution estimators
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  • Explore Bayesian estimation methods for Poisson processes
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This discussion is beneficial for statisticians, data analysts, and professionals in radioactive waste management who are involved in estimating decay rates and understanding the implications of different statistical methods.

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Homework Statement


I am given a table of ~3000 measurements for the number of decays from some radioactive substance within some period T. I want to estimate the mean number of decays per period.

Homework Equations


Poisson distribution
How to calculate mean.

The Attempt at a Solution


My intuition tells me that the best estimator is simply the to take the arithmetic mean of the dataset. However, given that the data should follow a poisson distribution I am not sure. When is the arithmetic mean the best estimator, and if this is indeed the case, why is it superior to for example fitting to a poisson distribution and then reading of the mean from the fit coefficients?
 
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aaaa202 said:
My intuition tells me that the best estimator is simply the to take the arithmetic mean of the dataset.
My experience is practical (radioactive waste management) and my intuition is the same as yours.
I'll be interested to see if our intuition is correct.
 
The search for leased biased estimators is fascinating math, but ignores the real world fact that you need to take into account how the answer will be used. An error one way may be more expensive than the same magnitude of error the other.
Also, taking a Bayesian view, I feel the attempt assumes some property of the a priori distribution of rates, symmetric perhaps.
Anyway, start here: https://en.wikipedia.org/wiki/Bias_of_an_estimator#Estimating_a_Poisson_probability
 
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