Appromixation of binomial coefficient for large numbers

In the context of writing a program for sortition, I would like to know if the entropy of my input random variable in large enough to potentially produce all outcome of my sortition problem.

Let say I have $n$ candidates, and I want to pick $k$ of them. $n \gg k$ ($n$ is in millions, $k$ in hundreds)

To process this sortition, I am using a binary input of $j$ bits. This input has an entropy of $2^j$ possible combinations.

I would like to know what $j$ to choose for a given $n, k$ so that : $$2^j \sim C(n, k)$$

For that, I would need an approximation of $C(n,k)$ for large numbers, expressed as exponentials.

I am aware of the Stirling's approximation, for expressing large factorials as exponentials. But I did not found similar approximation for binomial coefficients.

• If $k \ll n$, then ${n\choose k} \approx \dfrac{n^k}{k!}$ Aug 25 '14 at 15:27

As $n \to \infty$ with $k$ fixed, $C(n,k) \sim n^k/k!$.
Ok, thanks for you input. I realise that if $n >> k$, things are easier.
So if I want a upper limit, Let say for sorted sortition, then I consider for $k \to \infty$ $$A(n, k) \sim n^{k} \implies n^{k} \sim 2^j \implies e^{k \ln(n)} \sim e^{j \ln(2)} \implies j \sim \frac{k \ln(n)}{\ln(2)}$$
For instance, for $n=1,000,000$ and $k=100$, I need $j=1993$ bits of random input.