# Jeffreys prior for binomial likelihood

If I use a Jeffreys prior for a binomial probability parameter $\theta$ then this implies using a $\theta \sim beta(1/2,1/2)$ distribution.

If I transform to a new frame of reference $\phi = \theta^2$ then clearly $\phi$ is not also distributed as a $beta(1/2,1/2)$ distribution.

My question is in what sense is Jeffreys prior invariant to reparameterisations? I think I am misunderstanding the topic to be honest...

Best,

Ben

• Jeffreys' prior is invariant in the sense that starting with a Jeffreys prior for one parameterisation and running the appropriate change of variable is identical to deriving the Jeffreys prior directly for this new parameterisation. Actually, equivariant would be more appropriate a term than invariant. – Xi'an Apr 24 '17 at 20:47
• @ben18785: take a look at stats.stackexchange.com/questions/38962/… – Zen Apr 24 '17 at 21:48
• See also math.stackexchange.com/questions/210607/… (more or less the same question I think, but on a different site). – Nathaniel Apr 24 '17 at 23:16
• – Christoph Hanck Mar 6 at 7:49

Lets have $$\phi = g(\theta)$$, where $$g$$ is a monotone function of $$\theta$$ and let $$h$$ be the inverse of $$g$$, so that $$\theta = h(\phi)$$. We can obtain Jeffrey's prior distribution $$p_{J}(\phi)$$ in two ways:
1. Start with the Binomial model (1) $$$$\label{original} p(y | \theta) = \binom{n}{y} \theta^{y} (1-\theta)^{n-y}$$$$ reparameterize the model with $$\phi = g(\theta)$$ to get $$p(y | \phi) = \binom{n}{y} h(\phi)^{y} (1-h(\phi))^{n-y}$$ and obtain Jeffrey's prior distribution $$p_{J}(\phi)$$ for this model.
2. Obtain Jeffrey's prior distribution $$p_{J}(\theta)$$ from original Binomial model 1 and apply the change of variables formula to obtain the induced prior density on $$\phi$$ $$p_{J}(\phi) = p_{J}(h(\phi)) |\frac{dh}{d\phi}|.$$
To be invariant to reparameterisations means that densities $$p_{J}(\phi)$$ derived in both ways should be the same. Jeffrey's prior has this characteristic [Reference: A First Course in Bayesian Statistical Methods by P. Hoff.]
To answer your comment. To obtain Jeffrey's prior distribution $$p_{J}(\theta)$$ from the likelihood for Binomial model $$p(y | \theta) = \binom{n}{y} \theta^{y} (1-\theta)^{n-y}$$ we must calculate Fisher information by taking logarithm of likelihood $$l$$ and calculate second derivative of $$l$$ \begin{align*} l := \log(p(y | \theta)) &\propto y \log(\theta) + (n-y) \log(1-\theta) \\ \frac{\partial l }{\partial \theta} &= \frac{y}{\theta} - \frac{n-y}{1-\theta} \\ \frac{\partial^{2} l }{\partial \theta^{2}} &= -\frac{y}{\theta^{2}} - \frac{n-y}{ (1-\theta)^{2} } \end{align*} and Fisher information is \begin{align*} I(\theta) &= -E(\frac{\partial^{2} l }{\partial \theta^{2}} | \theta) \\ &= \frac{n\theta}{\theta^{2}} + \frac{n - n \theta}{(1-\theta)^{2}} \\ &= \frac{n}{\theta ( 1- \theta)} \\ &\propto \theta^{-1} (1-\theta)^{-1}. \end{align*} Jeffrey's prior for this model is \begin{align*} p_{J}(\theta) &= \sqrt{I(\theta)} \\ &\propto \theta^{-1/2} (1-\theta)^{-1/2} \end{align*} which is $$\texttt{beta}(1/2, 1/2)$$.
• I answered above by obtaining a Jeffrey's prior $p_{J}(\theta)$ from the likelihood for Binomial model. – Marko Lalović Apr 24 '17 at 20:36