End-User Probabilistic Programming [pdf] • Hacker News Board

Re: End-User Probabilistic Programming [pdf]

#11

Post by rahulravu96 » Wed, Jun 19, 2019, 8:11 PM UTC

cheerlessbog wrote: post #7

refrigerator wrote: post #5

Earlier quoted context omitted.

We're working on something like this: https://causal.app :) Guesstimate is awesome, but their team sadly stopped working on it a while back. It's definitely early days for automated inference, but I think giving people the tools to build "static" (non-learning) models that can account for uncertainty is hugely valuable in itself. You need serious gymnastics to do this in spreadsheets right now, and I wouldn't wish Ex…

Microsoft recently released a probabilistic programming library for .NET named Infer.NET [1] [1] https://github.com/dotnet/infer

They recently open sourced it. It was previewed in lectures from Christopher Bishop more than 10 years ago.

1) https://www.microsoft.com/en-us/research/project/infernet/ 2) http://videolectures.net/mlss09uk_bishop_ibi/

Re: End-User Probabilistic Programming [pdf]

#12

Post by eli_gottlieb » Wed, Jun 19, 2019, 8:15 PM UTC

Karrot_Kream wrote: post #9

eli_gottlieb wrote: post #4

Earlier quoted context omitted.

>As a side note, I've been doing more probabilistic programming with pymc3 recently, and it's pretty incredible how leaky the abstractions can be. I'm not saying there's a way to do better, just that at present there's a huge gap between the beautiful vision of "the inference button" and the current tools. Yeah, there's a pretty active debate in the probabilistic programming R&D community over whether it's a bug to s…

Paper's abstract looks cool, I'd love to take a look at this! Rather than selling "the inference button", I always wondered why more probabilistic frameworks don't play up the "programming" portion of it. Most of us who look into probabilistic programming aren't that afraid of getting our hands wet.

Hopefully we should have the actual framework open-sourced and a longer-form paper on arxiv (and under review) within a few months. This has been over a year in development by now, but we're getting there.

Re: End-User Probabilistic Programming [pdf]

#13

Post by floki999 » Wed, Jun 19, 2019, 10:14 PM UTC

Interesting but hardly novel. In certain fields, ‘end users’ have routinely employed probabilistic tools via Excel or other spreadsheets for decades. Wider adoption has been limited due to the knowledge base that is required to either (a) confidently build such models, (b) communicate probabilistic results to stakeholders.

@Risk and CrystalBall were some of the earlier Excel add-ins which simplified simulation-based spreadsheet development.

As someone else mentioned, the Excel/Python combination is really powerful, although lower-level. DataNitro comes to mind, as well as a product by Resolver Systems (?) which was essentially an IronPython powered spreadsheet interface.

Re: End-User Probabilistic Programming [pdf]

#14

Post by floki999 » Wed, Jun 19, 2019, 10:22 PM UTC

thanatropism wrote: post #2

It's nowhere as sophisticated as this, but I wrote a little utility for Monte Carlo analysis of spreadsheet models with Python and Xlwing: https://github.com/asemic-horizon/stanton As a bonus, since the spreadsheet model is exposed as a Python function, emulating complex spreadsheets with simple ML models (decision trees...) is easy.

Nice. The Excel/Python combo is very powerful. Microsoft has kept Excel in the dark ages, and could have implemented such better analytics than it currently offers. If they could integrate Python in lieu of VBA and make it compatible with their Form designer, it would become very popular. The ability to quickly knock-up desktop interfaces, closely integrated with powerful analytics and the user’s data source is priceless in certain environments (e.g. capital markets).

Re: End-User Probabilistic Programming [pdf]

#15

Post by axpence » Thu, Jun 20, 2019, 4:10 AM UTC

Let's say i want to predict an output `C` by multiplying two distributions `A``B` = `C`.

Assuming I am just guessing at the distribution of `A` and `B` (Uniform? Bernoulli? Geometric? Log-Normal?), would I get a better estimate by just multiplying `mean(A)` `mean(B)` ?

Point values suck. However, predicting the mean is often possible/realistic. And I feel like I am taking wild guess when describing a distribution of a data set to be honest.

TLDR: What results in better prediction/guestimate? multiplying incorrect probability distributions? Or multiplying more-correct means/point values?

Re: End-User Probabilistic Programming [pdf]

#16

Post by jmmcd » Thu, Jun 20, 2019, 8:59 PM UTC

axpence wrote: post #15

Let's say i want to predict an output `C` by multiplying two distributions `A` `B` = `C`. Assuming I am just guessing at the distribution of `A` and `B` (Uniform? Bernoulli? Geometric? Log-Normal?), would I get a better estimate by just multiplying `mean(A)` `mean(B)` ? Point values suck. However, predicting the mean is often possible/realistic. And I feel like I am taking wild guess when describing a distribution of…

I don't have a good answer. But I wonder if there are some realistic situations where we would have a good guess at the mean, but no clue about the distribution.