Glad to see this is out as well! Using probabilistic frameworks has the potential to eliminate a lot of the human error which can easily enter a large simulation. It's fair to say in the future probabilistic modules will become part of every standard library in every programming language, and distribution sampling functions will be as common as trig functions in a math library.
I am curious though how I would build up large queries in the BQL (SQL-like query language) or MML (meta-modeling language). For the orbital example, we conceivably only have a relatively low dimensional space. But what about a Bayes net for investigating genetic variants in a large genomic population? Doesn't this quickly become intractable?
I am curious though how I would build up large queries in the BQL (SQL-like query language) or MML (meta-modeling language). For the orbital example, we conceivably only have a relatively low dimensional space. But what about a Bayes net for investigating genetic variants in a large genomic population? Doesn't this quickly become intractable?