Demo: https://montesheet.singleton.io/
A simple proof-of-concept probabilistic spreadsheet in React with Material-UI.
It's a spreadsheet! Enter labels or numbers directly. Formulae begin with =. Reference other cells by name e.g. =A1+A2. Fill down by clicking on start cell, holding shift + down arrow to select the fill range, then press ctrl+d.
Where this spreadsheet is different is that you can assign probability distributions as values. You can use cells in math expressions whether they contain a scalar or a distribution. When two distributions are combined, a Monte Carlo simulation runs to generate a new value in that cell.
The following distribution functions are available:
=normal(mean, stddev)
A normal distribution (gaussian) with mean and standard deviation as specified.
=normal(mean, stddev, [min])
A normal distribution (gaussian) with mean and standard deviation as specified and a minimum sample value of min. Note that this can skew the distribution mean higher than the mean you specify as the center of the normal distribution. Any samples falling below the minimum value will be resampled.
=normal(mean, stddev, [min , max])
A normal distribution (gaussian) with mean and standard deviation as specified, a minimum sample value of min and a maximum sample value of max. Note that this can skew the distribution mean higher or lower than the mean you specify as the center of the normal distribution. Any samples falling outside of [min, max] will be resampled.
warning - montesheet is not yet smart enough to detect invalid min,max values for the given mean and stddev and will produce 0.0 for samples that didn't converge within [min,max]. Use with care.
=uniform(min, max)
A uniform distribution of floating point values >= min and < max.
=exponential(rate)
An exponential distribution of values with rate as specified.
For example you can use it to simulate when an event is going to happen next, given its average rate.
=poisson(mean)
A poisson distribution with a mean as specified.
For instance, a call center receives an average of 180 calls per hour, 24 hours a day. The calls are independent; receiving one does not change the probability of when the next one will arrive. The number of calls received during any minute has a Poisson probability distribution with mean 3: the most likely numbers are 2 and 3 but 1 and 4 are also likely and there is a small probability of it being as low as zero and a very small probability it could be 10.
All regular math operations (+,-,/,*) work on distributions. You can also use:
=mmax(distA, distB)
Samples pairs uniformly at random from distribution A and distribution B and chooses the maximum value each time. This is useful if you want to model e.g. latency of a process which always depends on the maximum latency of its subprocesses.
=choose(distA, distB)
Samples pairs uniformly at random from distribution A and distribution B and chooses one value from the pair with equal probability. Useful for creating bi-modal distributions from constitent parts.
The following functions are available
=min(dist), =mean(dist), =max(dist)
Compute the minimum, mean or maximum value respectively.
=p[0-9]+(dist) e.g. =p50(dist)
Computes the N/100th percentile value in the distribution. p50 computes the median and p99 computes the 99th percentile.
Expressions can be nested arbitrarily in formulae e.g. =normal(p50(exponential(5)), 1.0) is perfectly valid.
To re-generate the parser from grammar.jison
npm install jison -g
cd src/jison
jison grammar.jison
Which writes the file grammar.js in the same directory.
To run
npm install
Install the Netlify CLI
npm install netlify-cli -g
Run the development server
netlify dev
When the above command completes you'll be able to view your website at http://localhost:8888