alt Hacker NewsWhat would be a revolution in excel is if we could have probability distributions in cells. Right now all spreadsheets are fairly deterministic and we think that tells us something about the real world. Having an ability to make cells distributions (and then all resultant cells would be distributions too) would represent the uncertainty in the world better (and expose lots of interesting things like distributions of tails)
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Would those then be tensors?
Matrices are Tensors but with the matrix product operator instead of the tensor product operator.
Pandas supports MultiIndex DataFrames but the pandas docs recommend xarray for 3D and N-Dimensional data.
xarray supports N-Dimensional data as for example NetCDF but not tensor arithmetic.
xarray_jax: https://github.com/google-deepmind/xarray_jax :
> This library solves that problem. It registers xarray data structures as custom JAX PyTrees. This allows JAX to seamlessly flatten xarray objects into their raw arrays for accelerated computation and then unflatten the results back into fully labeled xarray objects, preserving critical metadata like dimension names and coordinates.
flatten and unflatten with datatypes is necessary for unrolling loops for performance.
Which is the correct logic for probabilistic logic, for expressions with frequentist or symbolic distributions as values? Are quantum logic and quantum statistical mechanics the appropriate or useful tools for all probabilistic logic?
dist_a1 <operator> dist_a2
uncertainties does mean±dev in Python with numpy types.
From https://news.ycombinator.com/item?id=41411280 :
> W3C CSVW supports per-column schema. ( with URIs for datatypes )
> Serialize a dict containing a value with uncertainties and/or Pint (or astropy.units) and complex values to JSON, then read it from JSON back to the same types. Handle datetimes, complex values, and categoricals
IEEE-754 specifies NaN (null), ±0, three infinities (positive, negative, and unsigned), but IEEE-754 does not specify a representation for categoricals, datetimes (like ISO8601), or complex numbers.
XSD (XML Schema Datatypes), which RDFS vocabularies often use to specify the rdfs:range of an rdfs:Property, does not specify how to specify abstract complex numbers; but OpenMath RDF, and QUDT (Quantities, Units, Dimensions, and Types) and OM Ontology all have a way to save complex numbers to disk, too.
Its a paid add-on but @Risk gives the functionality you're looking for
What would this look like? Whenever I need something like that, I just stick each parameter in its own cell and use functions from there.
I would love an easier way to communicate to tech/math-illiterate CEOs that worst case, average case, and best case are different things, but must all be planned for.
When I did Process Improvement we used MiniTab for this.
I honestly was not a fan of that program and would have loved to be able to use Excel, but there are so many statistical things you just can't do in it.
Guesstimate (https://getguesstimate.com) has been around for years. I assumed Excel and Google would incorporate its features quickly, but it never seems to have taken off.
Features:
- All cells are named (auto named if you don't set them)
- Cells can have single values, many types of probability distribution, or sample data
- Formulas also produce distributions as first class outputs, because calculation is a Monte Carlo sim
- Each cell also has a pop-up box that invites you to explain your reasoning -- built in documentation
It got so much right. Sample data particularly useful because you can build a probabilistic bottom-up forecast and then update it with real-world data.