I would be much more interested if this product were "choose probability density function centric." Then, the Monte Carlo engine would gain much more interest. Being able to choose or specify arbitrary distributions, and then run simulations, would be valuable.<p>Of special interest are non-continuous distributions. How often have normal distribution reasoning failed in finance?
Put another way, a user should be able to model a distribution himself.
Relevant—Uncertain<T>: A First-Order Type for Uncertain Data (Microsoft Research)<p><a href="http://research.microsoft.com/pubs/208236/asplos077-bornholtA.pdf" rel="nofollow">http://research.microsoft.com/pubs/208236/asplos077-bornholt...</a>
I'm convinced that an excel sort of lay-person's computing platform is where probabilistic programming will really take off. This seems really cool!
It's not really related but it made me think of a friend's PhD thesis on uncertain data. If the subject interests you, be sure to checkout the summary of his (impressive) work: <a href="http://a3nm.net/blog/phd_summary.html" rel="nofollow">http://a3nm.net/blog/phd_summary.html</a>.
I like it. I had to do a strategy session with a client a couple of weeks ago and we needed to estimate how much the strategy was likely to cost over the next few months the. We had quite a few variables to work with though. This would have been handy in such a scenario I presume? We knew what are components and the ranges were.
This is very similar to the paper
<a href="http://www.isi.edu/~szekely/contents/papers/2012/szekely2012-iui.pdf" rel="nofollow">http://www.isi.edu/~szekely/contents/papers/2012/szekely2012...</a><p>As per the paper , you can choose arbitrary distributions , construct a fluent graph , run Monte Carlo simulation and get the result - |via <a href="http://bit.ly/hnbuzz01" rel="nofollow">http://bit.ly/hnbuzz01</a> |
'Fuzzy logic' seems to be an ex-buzzphrase nowadays, but this seems pretty close to that territory. A variable/cell/logical-unit containing not a single value, but a distribution (often between bounds), and getting combined with other similar variables/cells/logical-units in ways that understand and respect the probability distributions.<p>Perhaps that field can provide a potential source of new names, when you decide to market this as a company.
Direct link to Github: <a href="https://github.com/getguesstimate/guesstimate-app" rel="nofollow">https://github.com/getguesstimate/guesstimate-app</a>
I was watching "Total time spent watching this video" video, and had a basic question.<p>How does one tell guesstimate that there's a hard lower bound on a quantity. ie. Video Length is at least 0, because negative watch times are unphysical? I know the specified distribution in this case is very narrow (the video lasting between -1 and 0 minutes has probability ~0.000032). But the answer does come out to be 26±32, which includes a substantial unphysical region.<p>And, if I give a hard lower bound on Video Length, can it propagate that knowledge into an asymmetric error on Total time?
Awesome!
I use Crystal Ball (<a href="http://www.oracle.com/us/products/applications/crystalball/overview/index.html" rel="nofollow">http://www.oracle.com/us/products/applications/crystalball/o...</a>) with triangular distributions and Monte Carlo for software project cost estimation. Crystal Ball costs thousands of dollars so I will be following this with interest.
I think this is super cool! We're so bad at estimating probabilities (think Han Solo's "never tell me the odds") that this helps visualize the distribution of outcomes
This is really cool. Can anyone recommend any particularly good/cogent Simple Caveman explanations of how Bayesoan theory/Monte Carlo simulation work?
What fun - I did a monte carlo estimate a few years back when trying to determine what purchases price of house my girlfriend and I could afford. It depended on probable interest rate, how much my old house would sell for, etc. It'd be interesting to see how simply it could be modeled in this.
Isn't how all software should be written? Expressions that represent a set of all possible values, effectively replacing the need for types.<p>Surely, such a platform would make building an app 100 times easier. Not that building apps is a good use of our resources.
Interesting idea - but you could certainly do this in any spreadsheet application with multiple cells to represent ranges etc.. I think the of estimating probabilities issue can be considered to be more of a practices issue than a tools issue.
This is a really great interface, and cool idea.<p>You might consider upping the run count, or maybe narrowing your bins for the visualization. Either way, it's great to see more tools embracing probability and uncertainty like this.
hey
nice tool
on a side note what tool did u use to create the animated tutorials on your git page:<a href="https://github.com/getguesstimate/guesstimate-app" rel="nofollow">https://github.com/getguesstimate/guesstimate-app</a><p>image link
<a href="https://camo.githubusercontent.com/8fd97a97fa656a1eb92294f0fc436885b5d8dbb3/687474703a2f2f672e7265636f726469742e636f2f6c636b496670416b69412e676966" rel="nofollow">https://camo.githubusercontent.com/8fd97a97fa656a1eb92294f0f...</a>
It'd be nice to be able to show / enter n-dimensional histograms too, so that one can get an idea of / control the correlation between two outputs / inputs.
Nice, but I can't help thinking of spreadsheets as something of a crutch.<p>Also check out:<p><a href="http://probcomp.csail.mit.edu/bayesdb/" rel="nofollow">http://probcomp.csail.mit.edu/bayesdb/</a><p><a href="https://github.com/taschini/pyinterval" rel="nofollow">https://github.com/taschini/pyinterval</a>
<a href="http://mavrinac.com/index.cgi?page=fuzzpy" rel="nofollow">http://mavrinac.com/index.cgi?page=fuzzpy</a>