Bayesian personalism models learning from experience as the updating of an agent's credence function on the information the agent acquires. The standard updating rules are hamstrung for zero probability events. The maneuvers that have been proposed to handle this problem are examined and found wanting: they offer only temporary relief but no satisfying and stable long term resolution. They do suggest a strategy for avoiding the problem altogether, but the price to be paid is a very crabbed account of learning from experience. I outline what Bayesians would need to do in order to come to grips with the problem rather than seeking to avoid it. Furthermore, I emphasize that an adequate treatment of the issues must work not only for classical probability but also for quantum probability as well, the latter of which is rarely discussed in the philosophical literature in the same breath with the updating problem. Since it is not obvious how the maneuvers applied to updating classical probability can be made to work for updating quantum probability a rethinking of the problem may be required. At the same time I indicate that in some special cases quantum probability theory has a self-contained solution to the problem of updating on zero probability events requiring no additional technical devices or rationality constraints
