I was looking up the entropy of a Student *t* distribution and something didn’t seem right, so I wanted to look at familiar special cases.

The Student *t* distribution with ν degrees of freedom has two important special cases: ν = 1 and ν = ∞. When ν = 1 we get the Cauchy distribution, and in the limit as ν → ∞ we get the normal distribution. The expression for entropy is simple in these two special cases, but it’s not at all obvious that the general expression at ν = 1 and ν = ∞ gives the entropy for the Cauchy and normal distributions.

The entropy of a Cauchy random variable (with scale 1) is

and the entropy of a normal random variable (with scale 1) is

The entropy of a Student *t* random variable with ν degrees of freedom is

Here ψ is the digamma function, the derivative of the log of the gamma function, and *B* is the beta function. These two functions are implemented as `psi`

and `beta`

in Python, and `PolyGamma`

and `Beta`

in Mathematica. Equation for entropy found on Wikipedia.

This post will show numerically and analytically that the general expression does have the right special cases. As a bonus, we’ll prove an asymptotic formula for the entropy along the way.

## Numerical evaluation

Numerical evaluation shows that the entropy expression with ν = 1 does give the entropy for a Cauchy random variable.

from numpy import pi, log, sqrt from scipy.special import psi, beta def t_entropy(nu): S = 0.5*(nu + 1)*(psi(0.5*(nu+1)) - psi(0.5*nu)) S += log(sqrt(nu)*beta(0.5*nu, 0.5)) return S cauchy_entropy = log(4*pi) print(t_entropy(1) - cauchy_entropy)

This prints 0.

Experiments with large values of ν show that the entropy for large ν is approaching the entropy for a normal distribution. In fact, it seems the difference between the entropy for a *t* distribution with ν degrees of freedom and the entropy of a standard normal distribution is asymptotic to 1/ν.

normal_entropy = 0.5*(log(2*pi) + 1) for i in range(5): print(t_entropy(10**i)- normal_entropy)

This prints

1.112085713764618 0.10232395977100861 0.010024832113557203 0.0010002498337291499 0.00010000250146458001

## Analytical evaluation

There are tidy expressions for the ψ function at a few special arguments, including 1 and 1/2. And the beta function has a special value at (1/2, 1/2).

We have ψ(1) = −γ and ψ(1/2) = −2 log 2 − γ where γ is the Euler–Mascheroni constant. So the first half of the expression for the entropy of a *t* distribution with 1 degree of freedom reduces to 2 log 2. Also, *B*(1/2, 1/2) = π. Adding these together we get 2 log 2 + log π which is the same as log 4π.

For large *z*, we have the asymptotic series

See, for example, A&S 6.3.18. We’ll also need the well-known fact that log(1 + *z*) ∼ *z*. for small *z*,

Next we use the definition of the beta function as a ratio of gamma functions, the fact that Γ(1/2) = √π, and the asymptotic formula here to find that

This shows that the entropy of a Student *t* random variable with ν degrees of freedom is asymptotically

for large ν. This shows that we do indeed get the entropy of a normal random variable in the limit, and that the difference between the Student *t* and normal entropies is asymptotically 1/ν, proving the conjecture inspired by the numerical experiment above.