Index: The Book of Statistical ProofsStatistical Models ▷ Count data ▷ Poisson-distributed data ▷ Log model evidence

Theorem: Let there be a Poisson-distributed data set $y = \left\lbrace y_1, \ldots, y_n \right\rbrace$:

\[\label{eq:Poiss} y_i \sim \mathrm{Poiss}(\lambda), \quad i = 1, \ldots, n \; .\]

Moreover, assume a gamma prior distribution over the model parameter $\lambda$:

\[\label{eq:Poiss-prior} p(\lambda) = \mathrm{Gam}(\lambda; a_0, b_0) \; .\]

Then, the log model evidence for this model is

\[\label{eq:Poiss-LME} \log p(y|m) = - \sum_{i=1}^n \log y_i ! + \log \Gamma(a_n) - \log \Gamma(a_0) + a_0 \log b_0 - a_n \log b_n \; .\]

and the posterior hyperparameters are given by

\[\label{eq:Poiss-post-par} \begin{split} a_n &= a_0 + n \bar{y} \\ b_n &= b_0 + n \; . \end{split}\]

Proof: With the probability mass function of the Poisson distribution, the likelihood function for each observation implied by \eqref{eq:Poiss} is given by

\[\label{eq:Poiss-LF-s1} p(y_i|\lambda) = \mathrm{Poiss}(y_i; \lambda) = \frac{\lambda^{y_i} \cdot \exp\left[-\lambda\right]}{y_i !}\]

and because observations are independent, the likelihood function for all observations is the product of the individual ones:

\[\label{eq:Poiss-LF-s2} p(y|\lambda) = \prod_{i=1}^n p(y_i|\lambda) = \prod_{i=1}^n \frac{\lambda^{y_i} \cdot \exp\left[-\lambda\right]}{y_i !} \; .\]

Combining the likelihood function \eqref{eq:Poiss-LF-s2} with the prior distribution \eqref{eq:Poiss-prior}, the joint likelihood of the model is given by

\[\label{eq:Poiss-JL-s1} \begin{split} p(y,\lambda) &= p(y|\lambda) \, p(\lambda) \\ &= \prod_{i=1}^n \frac{\lambda^{y_i} \cdot \exp\left[-\lambda\right]}{y_i !} \cdot \frac{ {b_0}^{a_0}}{\Gamma(a_0)} \lambda^{a_0-1} \exp[-b_0 \lambda] \; . \end{split}\]

Resolving the product in the joint likelihood, we have

\[\label{eq:Poiss-JL-s2} \begin{split} p(y,\lambda) &= \prod_{i=1}^n \frac{1}{y_i !} \prod_{i=1}^n \lambda^{y_i} \prod_{i=1}^n \exp\left[-\lambda\right] \cdot \frac{ {b_0}^{a_0}}{\Gamma(a_0)} \lambda^{a_0-1} \exp[-b_0 \lambda] \\ &= \prod_{i=1}^n \left(\frac{1}{y_i !}\right) \lambda^{n \bar{y}} \exp\left[-n \lambda\right] \cdot \frac{ {b_0}^{a_0}}{\Gamma(a_0)} \lambda^{a_0-1} \exp[-b_0 \lambda] \\ &= \prod_{i=1}^n \left(\frac{1}{y_i !}\right) \cdot \frac{ {b_0}^{a_0}}{\Gamma(a_0)} \cdot \lambda^{a_0 + n \bar{y} - 1} \cdot \exp\left[-(b_0 + n \lambda)\right] \\ \end{split}\]

where $\bar{y}$ is the mean of $y$:

\[\label{eq:y-mean} \bar{y} = \frac{1}{n} \sum_{i=1}^n y_i \; .\]

Note that the model evidence is the marginal density of the joint likelihood:

\[\label{eq:Poiss-ME} p(y) = \int p(y,\lambda) \, \mathrm{d}\lambda \; .\]

Setting $a_n = a_0 + n \bar{y}$ and $b_n = b_0 + n$, the joint likelihood can also be written as

\[\label{eq:Poiss-JL-s3} p(y,\lambda) = \prod_{i=1}^n \left(\frac{1}{y_i !}\right) \frac{ {b_0}^{a_0}}{\Gamma(a_0)} \frac{\Gamma(a_n)}{ {b_n}^{a_n}} \cdot \frac{ {b_n}^{a_n}}{\Gamma(a_n)} \lambda^{a_n-1} \exp\left[-b_n \lambda\right] \; .\]

Using the probability density function of the gamma distribution, $\lambda$ can now be integrated out easily

\[\label{eq:Poiss-ME-qed} \begin{split} \mathrm{p}(y) &= \int \prod_{i=1}^n \left(\frac{1}{y_i !}\right) \frac{ {b_0}^{a_0}}{\Gamma(a_0)} \frac{\Gamma(a_n)}{ {b_n}^{a_n}} \cdot \frac{ {b_n}^{a_n}}{\Gamma(a_n)} \lambda^{a_n-1} \exp\left[-b_n \lambda\right] \, \mathrm{d}\lambda \\ &= \prod_{i=1}^n \left(\frac{1}{y_i !}\right) \frac{\Gamma(a_n)}{\Gamma(a_0)} \frac{ {b_0}^{a_0}}{ {b_n}^{a_n}} \int \mathrm{Gam}(\lambda; a_n, b_n) \, \mathrm{d}\lambda \\ &= \prod_{i=1}^n \left(\frac{1}{y_i !}\right) \frac{\Gamma(a_n)}{\Gamma(a_0)} \frac{ {b_0}^{a_0}}{ {b_n}^{a_n}} \; , \end{split}\]

such that the log model evidence is shown to be

\[\label{eq:Poiss-LME-qed} \log p(y|m) = - \sum_{i=1}^n \log y_i ! + \log \Gamma(a_n) - \log \Gamma(a_0) + a_0 \log b_0 - a_n \log b_n \; .\]

Metadata: ID: P227 | shortcut: poiss-lme | author: JoramSoch | date: 2020-04-21, 09:09.