conditional models #216
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conditional models #216
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@@ Coverage Diff @@
## master #216 +/- ##
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+ Coverage 28.53% 30.99% +2.45%
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Files 43 45 +2
Lines 1265 1381 +116
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+ Hits 361 428 +67
- Misses 904 953 +49
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WIP-- DO NOT MERGE
Currently we write, e.g.,
There's an implicit step here of creating the conditional distribution of
m(x=x)givenobservations, but we currently have no way to write this. Also, it would be useful to move closer to Turing's representation for sampling, which doesn't require observations in a separate argument.At the same time, we need to keep our generative representation. So this is a start on letting us write
Note that the
|here is usually a bitwiseor. But I don't see this being used for models, so the overloading feels safe for this usage.With this change, we now have
Model,JointDistribution, and alsoConditional. Duplicating methods is starting to seem a bit much. So let's addAbstractModeland define operations on that whenever possible.For now, we have
AbstractModel <: Distribution, but we'll soon move this to<: MeasureTheory.Measure