Abstract
You encountered the Poisson distribution in problems at the end of the previous chapter. The Poisson distribution is useful when the random variable is a count of the number of rare events occurring per unit time, unit volume, unit distance, etc. For example, the number of new cases of rhabdomyosarcoma (a rare form of cancer) occurring in Johnson County, Iowa, each year might be represented as a Poisson random variable.
Keywords
- Mercury Concentration
- Posterior Density
- Inverse Gamma
- Poisson Random Variable
- Posterior Predictive Distribution
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Cowles, M.K. (2013). Other One-Parameter Models and Their Conjugate Priors. In: Applied Bayesian Statistics. Springer Texts in Statistics, vol 98. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5696-4_6
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