(5thPercentile: number) to (95thPercentile: number)
to(5thPercentile: number, 95thPercentile: number)
to function is an easy way to generate simple distributions using predicted 5th and 95th percentiles.
If both values are above zero, a
lognormal distribution is used. If not, a
normal distribution is used.
- 5 to 10
- -5 to 5
- 1 to 10000
5 to 10is entered, both numbers are positive, so it generates a lognormal distribution with 5th and 95th percentiles at 5 and 10.
5 to 10does the same thing as
-5 to 5is entered, there's negative values, so it generates a normal distribution. This has 5th and 95th percentiles at 5 and 10.
95thPercentile: number, greater than
If you haven't tried calibration training, you're likely to be overconfident. We recommend doing calibration training to get a feel for what a 90 percent confident interval feels like.
mixture(...distributions: Distribution, weights?: number)
mx(...distributions: Distribution, weights?: number)
mixture(distributions: Distributions, weights?: number)
mx(distributions: Distributions, weights?: number)
mixture mixes combines multiple distributions to create a mixture. You can optionally pass in a list of proportional weights.
- With Weights
- With Continuous and Discrete Inputs
- Array of Distributions Input
distributions: A set of distributions or numbers, each passed as a paramater. Numbers will be converted into point mass distributions.
weights: An optional array of numbers, each representing the weight of its corresponding distribution. The weights will be re-scaled to add to
1.0. If a weights array is provided, it must be the same length as the distribution paramaters.
Special Use Cases of Mixtures
🕐 Zero or Continuous
One common reason to have mixtures of continous and discrete distributions is to handle the special case of 0. Say I want to model the time I will spend on some upcoming project. I think I have an 80% chance of doing it.
In this case, I have a 20% chance of spending 0 time with it. I might estimate my hours with,
🔒 Model Uncertainty Safeguarding
One technique several Foretold.io users used is to combine their main guess, with a "just-in-case distribution". This latter distribution would have very low weight, but would be very wide, just in case they were dramatically off for some weird reason.
Creates a normal distribution with the given mean and standard deviation.
- normal(100000000000, 100000000000)
standard deviation: Number greater than zero
lognormal(mu: number, sigma: number)
Creates a log-normal distribution with the given mu and sigma.
sigma represent the mean and standard deviation of the normal which results when
you take the log of our lognormal distribution. They can be difficult to directly reason about.
Because of this complexity, we recommend typically using the to syntax instead of estimating
sigma: Number greater than zero
mu and sigma
The log of
lognormal(mu, sigma) is a normal distribution with mean
muand standard deviation
sigma. For example, these two distributions are identical:
Creates a uniform distribution with the given low and high values.
high: Number greater than
While uniform distributions are very simple to understand, we find it rare to find uncertainties that actually look like this. Before using a uniform distribution, think hard about if you are really 100% confident that the paramater will not wind up being just outside the stated boundaries.
One good example of a uniform distribution uncertainty would be clear physical limitations. You might have complete complete uncertainty on what time of day an event will occur, but can say with 100% confidence it will happen between the hours of 0:00 and 24:00.
Creates a discrete distribution with all of its probability mass at point
Few Squiggle users call the function
pointMass() directly. Numbers are converted into point mass distributions automatically, when it is appropriate.
For example, in the function
mixture(1,2,normal(5,2)), the first two arguments will get converted into point mass distributions
with values at 1 and 2. Therefore, this is the same as
pointMass() distributions are currently the only discrete distributions accessible in Squiggle.
- normal(5,2) * 6
- dotAdd(normal(5,2), 6)
- dotMultiply(normal(5,2), 6)
- beta(10, 20)
- beta(1000, 1000)
- beta(1, 10)
- beta(10, 1)
- beta(0.8, 0.8)
alpha: Number greater than zero
beta: Number greater than zero
Caution with small numbers
Squiggle struggles to show beta distributions when either alpha or beta are below 1.0. This is because the tails at ~0.0 and ~1.0 are very high. Using a log scale for the y-axis helps here.
- beta(0.3, 0.3)
- beta(0.5, 0.5)
- beta(0.8, 0.8)
- beta(0.9, 0.9)
Creates an exponential distribution with the given rate.
rate: Number greater than zero
triangular(low:number, mode:number, high:number)
Creates a triangular distribution with the given low, mode, and high values.
mode: Number greater than
high: Number greater than
Creates a sample set distribution using an array of samples.
samples: An array of at least 5 numbers.