Specifically, I want to express a model in analytic terms:
x = 2y + z^2
provide a dataset of x, y, and z tuples with associated error distributions, and ask questions like "what is three-sigma confidence interval for the model given this dataset", and "what would tunable parameters a and b have to be for the most consistent account".
Ideally, it'd be able to take into account convolutions, as well.
_The Art of Prolog_'s chapter on meta-interpreters* includes an interpreter with uncertainty thresholds. It's about a quarter-page of code - It's on page 318, in the first edition (what I have on hand). Extending it to support statistical significance rather than just a 0-1.0 confidence interval shouldn't be too hard. Combining that with constraint programming would probably suffice.
* Rather like chapter 4 of SICP, on making metacircular evaluator Prologs.
Specifically, I want to express a model in analytic terms:
x = 2y + z^2
provide a dataset of x, y, and z tuples with associated error distributions, and ask questions like "what is three-sigma confidence interval for the model given this dataset", and "what would tunable parameters a and b have to be for the most consistent account".
Ideally, it'd be able to take into account convolutions, as well.
_The Art of Prolog_'s chapter on meta-interpreters* includes an interpreter with uncertainty thresholds. It's about a quarter-page of code - It's on page 318, in the first edition (what I have on hand). Extending it to support statistical significance rather than just a 0-1.0 confidence interval shouldn't be too hard. Combining that with constraint programming would probably suffice.
* Rather like chapter 4 of SICP, on making metacircular evaluator Prologs.