Bayesian calculation of unspecified probabilities from priors

The functionality for this may go in sssom-py but it seems logical to put anything involving probabilistic calculations into an issue here

currently boomer assumes the user specifies priors for all 4 possibilities

What if we have a file which we have a mapping with a probability specified for only one interpretation? In this case we should use standard probability axioms to calculate other probabilities based on priors of probability of any mapping having a particular interp

E.g. assuming global priors

P(equiv) = 0.8
P(sub) = 0.05
P(sup) = 0.05
P(sib) = 0.1

assume sssom contains equiv statement with confidence 0.4

```
P(sub | equiv) = 0.0
P(sub | NOTequiv) = P(NOTequiv | sub) . P(sub)
----
P(NOTequiv)

= 1 * 0.05
---
0.2

= 0.25
```

therefore posterior p(sub) = 0.4*0 + 0.6 * .25 = 0.15

all posterior

p(equiv) = 0.4
p(sub) = 0.15
p(sup) = 0.15
p(sub) = 0.3

The issue does not name a file, test, or entry point; first determine whether this belongs in boomer or sssom-py and review the existing handling of priors. Done means the selected component can handle mappings with unspecified interpretation probabilities using the stated priors and has coverage for that case.

Written by the indexing model from the issue text.