# Why do we do Bayesian Inference about function parameters?

I have been reading about Bayesian inference by Han Liu and Larry Wasserman. In the section 12.2.3 they defined a bayesian inference on a variable parameterised by a function.

Given a random variable $$X \sim Berouli(\theta)$$ and $$D_n = \{X_1,X_2,...X_n\}$$ the set of observed data, and $$\psi = log(\frac{\theta}{1 - \theta})$$. Also let $$\pi(\theta) = 1$$, then posterior distribution for $$\theta$$ is equal to a $$Beta \sim (S_n + 1, n-S_n +1)$$ distributed, where $$S_n = \sum_{i=1}^nX_i$$, the number of successes.

The posterior is $$p(\theta|D) = \frac{\Gamma(n+2)}{\Gamma(S_n+1)\Gamma(n-S_n+1)}\theta^{S_n}\theta^{n - S_n}$$We can also find the posterior of $$\psi$$ by substituting $$\theta$$ with $$\psi$$ to get
$$p(\psi|D) = \frac{\Gamma(n+2)}{\Gamma(S_n+1)\Gamma(n-S_n+1)}({\frac{e^{\psi}}{1+e^{\psi}}})^{S_n}(\frac{e^{\psi}}{1+ e^{\psi}})^{n - S_n}$$

To sample from $$p(\psi|D)$$ we can sample from $$p(\theta|D)$$ and compute $$\psi$$ to obtain samples for $$p(\psi|D)$$.

Although this question may seem stupid.. I would like to know where such instances of computing posterior of functions of random variables being used in Bayesian Inference ?

Also, another point im not sure is why the authors decided to define an equation for the posterior CDF of the function $$\tau = g(\theta)$$. Why are we interested in a posterior CDF ?

• The book in section 12.2.3 talks about the posterior cdf of the transformed variable though.. Quote "Given the data $D_n = {X_1, . . . , X_n}$, how do we make inferences about a function $\tau = g(\theta)$? The posterior CDF for $\tau$ is ..." – calveeen Oct 13 at 6:35