Introduction To Non-Informative Priors

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Prior density is denoted by $g(.)$ in this article

Introduction

Non-Informative Priors are the priors which we assume when we do not have any belief about the parameter let say $ \theta $ . This leads noninformative priors to not favor any value of $ \theta $ , which gives equal weights to every value that belongs to $\Theta$. for example let us we have three hypothesis , so the prior which attach weight of $ \frac{1}{3}$ to each of the hypothesis is noninformative prior.

Note : most of the noninformative priors are improper.

An Example

Now let us assume a simple example let us assume our parameter space $\Theta$ is a finite set containing n elements such as

\[{\theta_1,\theta_2,\theta_3,\theta_4....\theta_n} \ \in \ \Theta\]

Now the obvious weight given to each $\theta_i$ when we have not any prior beliefs is $\frac{1}{n}$ that gives us prior is proportional to a constant because $\frac{1}{n}$ is a constant let us say $\frac{1}{n}$=c hence we can say

\[g(\theta) = c\]

Now let us assume a transformation $\eta=e^{\theta} $ , that is $\theta = log(\eta)$ . If $ g(\theta)$ is the density of $\theta$ then we can write density of $\eta$ as

\[g^*(\eta)=g(\theta)\frac{d\theta}{d\eta} \\ g^*(\eta)=g(log \ \eta)\frac{d \ log \ \eta }{d\eta} \\ g^*(\eta)=\frac{g(log \ \eta)}{\eta} \\ g^*(\eta) \propto \frac{1}{\eta}\]

Thus if we choose prior for $\theta$ as constant , then we have to assume prior for $\eta$ as proportional to $\eta^{-1}$ to arrive at the same answer in both cases either we take $\theta $ or $\eta$ . Thus we cannot maintain consistency and assume both prior proportional to constant . This leads to the search of such noninformative priors which are invariant under transformations.

Noninformative Priors for Location Parameter

A Parameter is said to be location parameter if the density $f(x ; \theta)$ can be written as a function of $(x - \theta)$

Let X is a random variable with location parameter $\theta$ then density can be written as $h(x- \theta)$. Just assume instead of observing X we observed Y = X+c and let us take $\eta=\theta+c$ then can see that the density of Y is given by $h(y - \eta)$. Now $(X,\theta) \ and (Y,\eta)$ have same parameter and sample space which gives us the idea that they must have same noninformative prior

Let $g$ and $g^*$ are noninformative priors for $(X,\theta) \ and (Y,\eta)$ respectively. So according to our argument both will have same noninformative priors , let us assume a subset of real line A

\[P^g(\theta \ \in \ A ) = P^{g^*}(\eta \ \in \ A )\]

Now we have assumed $\eta=\theta+c$ so

\[P^{g^*}(\eta \ \in \ A )=P^{g}(\theta +c \ \ \in \ A )=P^{g}(\theta \ \in \ A-c )\]

which leads us to

\[P^{g}(\theta \ \in \ A)=P^{g^*}(\theta \ \in \ A-c ) \tag{*}\\ \int_Ag(\theta)d\theta=\int_{A-c}g(\theta)d\theta=\int_Ag(\theta-c)d\theta\]

It holds for any set A of real line , and any c on real line so it lead us to

\[g(\theta)=g(\theta-c)\]

Now if we take $\theta=c$ we get $g(c)=g(0)$ ,and we know it is true for all c , it leads us to the conclusion that the prior in the case of location parameter is constant functions , for simplicity most of the statistician assume it equal to 1 , $g(.) = 1$

Noninformative Priors for Scale Parameter

A Parameter is said to be location parameter if the density $f(x ; \theta)$ can be written as a $\frac{1}{\theta}h(\frac{x}{\theta})$ where $\theta>0$

For example in normal distribution we $N(\mu,\sigma^2)$ , $\sigma$ is a scale parameter .

To get noninformative prior for Scale Parameter $\theta$ of a random variable X , instead of observing X we observe $Y = cX$ for any $c > 0 $ , let us define $\eta = c\sigma$ , so then the density of $Y $ is given by $\frac{1}{\eta}f(\frac{1}{\eta})$ .

Now similar to previous part here $(X,\theta)$ and $(Y,\eta)$ have same sample and parameter space , so both will have same noninformative priors. Let $g$ and $g^*$ are noninformative priors for $(X,\theta) \ and (Y,\eta)$ respectively. So according to our argument both will have same noninformative priors

\[P^g(\theta \in A)= P^{g^*}(\theta \in A)\]

Here A is a subset of Positive real line, i.e $A \subset R^+$ , now putting $\eta = c\sigma$

\[P^{g^*}(\eta \in A) = P^g(\theta \in \frac{A}{c}) \\ P^g(\theta \in A) = P^g(\theta \in \frac{A}{c}) \\ \int_Ag(\theta)d\theta=\int_{\frac{A}{c}}g(\theta)d\theta=\int_A\frac{1}{c}g(\frac{\theta}{c})d\theta\]

so

\[g(\theta)=\frac{1}{c}g(\frac{\theta}{c})\]

Now taking $\theta=c$ , we get

\[g(c)=\frac{1}{c}g(1)\]

Now this equation is true for any value $c>0$ so , for convenience taking $g(c)=1$ , it gives us noninformative prior $g(\theta)= \frac{1}{\theta}$

Note : It is an improper prior , $\int_0^{\infty}\frac{1}{\theta}d\theta = \infty $

Flaw and introduction of relatively location invariant prior

Now we know noninformative prior for both Scale and Location parameter, but there is flaw . The prior we get for location and scale parameter in previous part are improper priors . If two random variables have identical form , then they have same non informative priors . but the problem here is due to improper priors , noninformative priors are not unique. lets say we have an improper prior g then if we multiply g by any constant k then the resultant gk will give same bayesian decisions as g.

Now in previous parts we have assumed two priors $g$ and $g^* $ , but we do not need that , we can get $g^*$ by just multiplying $g$ by a constant and vice-versa.

Now equation $(*)$ can be written as

\[P^g(A)=l(k)P^{g}(A-c)\]

Where $l(k)$ is some positive function ,

\[\int_Ag(\theta)d\theta=l(k)\int_{A-c}g(\theta)d\theta=l(k)\int_Ag(\theta-c)d\theta\]

It holds for all A , so $g(\theta)=l(k)g(\theta-c)$ , and taking $\theta=c$ give us $l(k)=\frac{g(c)}{g(0)}$ , putting this value back will give us

\(g(\theta-c)=\frac{g(0)g(\theta)}{g(c)} \tag{**}\) Now there is a lot of prior other than $g(\theta)=c$ , which satisfy equation (** ) , so any prior of this form will be know as relatively location invariant