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1) The simplest explanation is the most probable, so the distribution of probabilities for hypotheses looks like: 0.75, 0.12, 0.04 .... if hypothesis are ordered from simplest to more complex.
2) The simplest explanation is the just more probable, so the distribution of probabilities for hypotheses looks like: 0.09, 0.07, 0.06, 0.05.
The interesting feature of the second type is that simplest explanation is more likely to be wrong than right (its probability is less than 0.5).
Different types of Occam razor are applicable in different situations. If the simplest hypothesis is significantly simpler than others, it is the first case. If all hypothesis are complex, it is the second. First situation is more applicable some inherently simple models, e.g. laws of physics or games. The second situation is more about complex situation real life.
But "all the other explanations combined" was talking about the probabilities. We're not combining the explanations, that wouldn't make any sense.
The only ontology that is required is Bayesianism, where explanations can have probabilities of being correct.
Ok, tabooing the word ontology here. All that's needed is an understanding of Bayesianism to answer the question of how you combine the chance of all other explanations.