Discrete case: coin flipping

Consider a coin with head (H) and tail (T). Denote this information by I. For a given coin flip, denote the probability of an outcome of heads as P(H|I). Denote the probability of an outcome of tails by P(T|I).

In applying the desideratum, consider the information contained in the event of the coin flip as framed. It describes no distinction between heads and tails. Given no other information, the elements "head" and "tail" are interchangeable. Application of the desideratum then demands that:

${\displaystyle P(H|I)=P(T|I)}$

The probabilities must add to 1, thus:

${\displaystyle P(H|I)+P(T|I)=1\rightarrow 2P(H|I)=1\rightarrow P(H|I)=0.5}$.

This argument extends to N categories, to give the "flat" prior probability 1/N.

This provides a consistency-based argument for the principle of indifference: If someone is truly ignorant about a discrete/countable set of outcomes apart from their potential existence, but does not assign them equal prior probabilities, then they are assigning different probabilities when given the same information.

This can be alternatively phrased as: a person who does not use the principle of indifference to assign prior probabilities to discrete variables, is either not ignorant about them, or reasoning inconsistently.

Continuous Case: Location Parameter

This is the easiest example for continuous variables. It is given by stating one is "ignorant" of the location parameter in a given problem. The statement that a parameter is a "location parameter" is that the sampling distribution, or likelihood of an observation X depends on a parameter ${\displaystyle \mu }$ only through the difference.

${\displaystyle p(X|\mu ,I)=f(X-\mu )}$

for some normalized, but otherwise distribution f(.).

Note that the given information that f(.) is a normalized distribution is a significant prerequisite to obtaining the final conclusion of a uniform prior, because uniform probability distributions can only be normalized given a finite input domain. In other words, the assumption that f(.) is normalized is implicitly also requiring that the location parameter ${\displaystyle \mu }$ does not extend to infinity in any of its dimensions. Otherwise, the uniform prior would not be normalisable.

Examples of location parameters include the mean parameter of a normal distribution with known variance and the median parameter of a Cauchy distribution with a known interquartile range.

The two "equivalent problems" in this case, given one's knowledge of the sampling distribution ${\displaystyle p(X|\mu ,I)=f(X-\mu )}$, but no other knowledge about ${\displaystyle \mu }$, is simply given by a "shift" of equal magnitude in X and ${\displaystyle \mu }$. This is because of the relation:

${\displaystyle f(X-\mu )=f([X+b]-[\mu +b])=f(X^{(1)}-\mu ^{(1)})}$

So simply "shifting" all quantities up by some number b and solving in the "shifted space" and then "shifting" back to the original one should give exactly the same answer as if we just worked on the original space. Making the transformation from ${\displaystyle \mu }$ to ${\displaystyle \mu ^{(1)}}$ has a Jacobian of simply 1, and so the prior probability ${\displaystyle g(\mu )=p(\mu |I)}$ must satisfy the functional equation:

${\displaystyle g(\mu )=\left|{\partial \mu ^{(1)} \over \partial \mu }\right|g(\mu ^{(1)})=g(\mu +b)}$

And the only function that satisfies this equation is the "constant prior":

${\displaystyle p(\mu |I)\propto 1}$

Thus, the uniform prior is justified for expressing complete ignorance of a normalized prior distribution on a finite, continuous location parameter.

Continuous case: scale parameter

As in the above argument, a statement that ${\displaystyle \sigma }$ is a scale parameter means that the sampling distribution has the functional form:

${\displaystyle p(X|\sigma ,I)={1 \over \sigma }f\left({X \over \sigma }\right)}$

Where, as before, ${\displaystyle f(\cdot )}$ is a normalized probability density function. The requirement that probabilities be finite and positive forces the condition ${\displaystyle \sigma >0}$. Examples include the standard deviation of a normal distribution with a known mean or the gamma distribution. The "symmetry" in this problem is found by noting that.

${\displaystyle {X \over \sigma }={Xa \over \sigma a};a>0}$

and setting ${\displaystyle X^{(1)}=Xa}$ and ${\displaystyle \sigma ^{(1)}=\sigma a.}$ But, unlike in the location parameter case, the Jacobian of this transformation in the sample space and the parameter space is a, not 1. So, the sampling probability changes to:

${\displaystyle p(X^{(1)}|\sigma ,I)={1 \over a}\cdot {1 \over \sigma }f\left({Xa \over \sigma a}\right)={1 \over \sigma ^{(1)}}f\left({X^{(1)} \over \sigma ^{(1)}}\right)}$

Which is invariant (i.e. has the same form before and after the transformation), and the prior probability changes to:

${\displaystyle p(\sigma |I)={1 \over a}p(\sigma ^{(1)}|I)={1 \over a}p\left({\sigma \over a}|I\right)}$

Which has a unique solution (up to a proportionality constant):

${\displaystyle p(\sigma |I)\propto {1 \over \sigma }\rightarrow p(\log(\sigma )|I)\propto 1}$

Which is well-known Jeffreys prior for scale parameters, which is "flat" on the log scale, although it is derived using a different argument to that here, based on the Fisher information function. The fact that these two methods give the same results, in this case, does not imply it in general.

Continuous case: Bertrand's paradox

Edwin Jaynes used this principle to provide a resolution to Bertrand's Paradox^[2] by stating his ignorance about the exact position of the circle.