Problems with zero probability events in Bayesian Networks In the book Probabilistic Graphical Models: Principles And Techniques by Daphne Koller, the author at one place (Box 3c), states the challenges in picking probabilities for a Bayesian network model. One of the challenges stated, is in assigning zero probabilities to events. Am finding difficulty interpreting the author's statement. Am quoting the exact text of the section below.

Zero probabilities: A common mistake is to assign a probability of
zero to an event that is extremely unlikely, but not impossible. The
problem is that one can never condition away a zero probability, no
matter how much evidence we get.

What is meant by "condition away"? Please clarify this above point.

Presumably what they mean here is that for any observable data $\mathbf{x}$ and any hypothesis $\mathcal{H}$ you have:
$$\mathbb{P}(\mathcal{H}) = 0
\quad \quad \implies \quad \quad
\mathbb{P}(\mathcal{H}|\mathbf{x}) = 0.$$
That is, if you assume that an event has prior probability zero then its posterior probability will still be zero.
