# Posterior distribution of parameter

I am looking at pg. 257 of the textbook "Statistical computing with R" by Maria L. Rizzo, where an example is given of a simple model of stock returns:

We have 5 stocks, and at the end of a year of 250 trading days, we have a vector [$x_1$, $x_2$, ..., $x_5$], where $x_i$ is the number of days that stock $i$ was the best performing stock. This is a multinomial joint distribution, with probability vector $p = [\frac{1}{3}, \frac{1 - \beta}{3}, \frac{1 - 2\beta}{3}, \frac{2\beta}{3}, \frac{\beta}{3} ]$, with $\beta \in (0, 0.5)$.

Now, what I don't understand, is that in the textbook they now say that the posterior probability distribution of $\beta$, which now represents the parameter of the multinomial distribution here, is

$Pr[\beta | (x_1, ..., x_5)] = \frac{250!}{x_1!x_2!x_3!x_4!x_5!}p_1^{x_1}p_2^{x_2}p_3^{x_3}p_4^{x_4}p_5^{x_5}$

which is also the pmf (probability mass function) of the multinomial distribution, (given the parameter) i.e.

$Pr[(x_1, ..., x_5) | \beta] = \frac{250!}{x_1!x_2!x_3!x_4!x_5!}p_1^{x_1}p_2^{x_2}p_3^{x_3}p_4^{x_4}p_5^{x_5}$

According to Bayes

$Pr[\beta | (x_1, ..., x_5)] = \frac{Pr[(x_1, ..., x_5) | \beta]Pr[\beta]}{Pr[(x_1, ..., x_5)]}$

So the only way $Pr[\beta | (x_1, ..., x_5)] = Pr[ (x_1, ..., x_5) | \beta]$ then is if $Pr[\beta] = Pr[(x_1, ..., x_5)]$, but I am assuming here that $\beta$ is uniformly distributed, making $Pr[\beta]$ a constant. $Pr[(x_1, ..., x_5)]$ is not a constant, if I am correct, because of combinatorics.

So what am I missing here?

• The data are fixed and so the (marginal) probability of the data is a constant. – mef Mar 6 '16 at 15:13
• @mef: as a function of $(x_1,x_2,\ldots,x_5)$ the normalising constant of the posterior on $\beta$ varies for different samples $(x_1,x_2,\ldots,x_5)$. – Xi'an Mar 6 '16 at 15:24

While I do not have access to this book, I think the first equation is missing a proportionality constant, i.e., that the posterior on $\beta$ should be \begin{align*}\pi(\beta | (x_1, ..., x_5)) &\propto \dfrac{250!}{x_1!x_2!x_3!x_4!x_5!}p_1^{x_1}p_2^{x_2}p_3^{x_3}p_4^{x_4}p_5^{x_5}\\ &\propto p_2(\beta)^{x_2}p_3(\beta)^{x_3}p_4(\beta)^{x_4}p_5(\beta)^{x_5}\\&\propto \{1 - \beta\}^{x_2}\, \{1 - 2\beta\}^{x_3}\, \beta^{x_4}\, \beta^{x_5}\\&\propto \{1 - \beta\}^{x_2}\, \{1 - 2\beta\}^{x_3}\, \beta^{x_4+x_5}\end{align*} with a manageable normalising constant since this is a polynomial in $\beta$. You are right that the marginal $p(x_1, ..., x_5)$ is not a constant. For instance, \begin{align}\int_0^{1/2} \{1 - \beta\}^{3}\, &\{1 - 2\beta\}^{6}\, \beta^{9} \text{d}\beta = (1/2)^{10}/10 - (15(1/2)^{11})/11 \\ &+ (33(1/2)^{12})/4 - 29(1/2)^{13} + (456(1/2)^{14})/7 - (484(1/2)^{15})/5\\ &+ 95(1/2)^{16} - (1008(1/2)^{17})/17 + (64(1/2)^{18})/3 - (64(1/2)^{19})/19 \\ &= 4.38\, 10^{-9} \end{align} and \begin{align}\int_0^{1/2} \{1 - \beta\}^{1}\, \{1 - 2\beta\}^{2}\, \beta^{3} \text{d}\beta &= (1/2)^4/4 - (1/2)^5 + (4(1/2)^6)/3 - (4(1/2)^7)/7 \\ &= 7.44\, 10^{-4} \end{align} For a similar problem with a Gibbs sampling resolution, see Tanner & Wong (1987) [also processed in our book, pp.347-348].