Why is the value of $p_i\log(p_i)$ in entropy formula considered $0$ when $p_i =0$? I get that it is the limiting value, but does that mean $p$ is never equals to zero, but only tends to?


2 Answers 2


Two possible answers:

  • If a symbol has probability zero, then it does not influence the calculation and there is no need to include it, so you never actually calculate $0\log(0)$.

  • If you insist in including symbols with zero probability, then $0\log(0)=0$ by convention.

  • $\begingroup$ it doesn't have to be "by convention". the limit $$ \lim_{p \to 0} p \log(p) = 0 $$ $\endgroup$ Commented Mar 26, 2021 at 16:33
  • 1
    $\begingroup$ @robertbristow-johnson I would agree only if you write the limit with $p \rightarrow 0^+$. But the limit itself (the "two-sided limit") does not exist, as far as I understand these things. $\endgroup$
    – MBaz
    Commented Mar 26, 2021 at 17:12
  • $\begingroup$ yeah you're right. too late to change it. oops. $\endgroup$ Commented Mar 26, 2021 at 19:03

I am not adding a lot to MBaz, mostly graphical hints. It can be interesting to look at the elementary function behind Shannon entropy: $H: p\mapsto -p \log p -(1-p) \log (1-p)$, displayed below:

Shannon Entropy for bernoulli probability

While it seems not defined at $p=0$ or $p=1$, the function $H$ is very symmetric and behaves quite well at $0$ and $1$ for a Bernoulli trial. Here, we talk about a binary event ($X$ happens with probability $p=P(X)$. The converse event is "not $X$". While entropy is often described as a measure of information, it can be seen as a measure of uncertainty. If $X$ is always equal to 1, it is certain. If $X$ never occurs, its converse is certain as well. In both cases, we have not surprise: the uncertainty is zero, and the "definitions" $-p \log p = 0$ for $p=0$ or $-(1-p) \log (1-p) =0$ for $p=1$ make sense.

More details can be found in Entropy is a measure of uncertainty.


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