probability_calculator

Calculate with and analyze probability densities.

Usage

Initialization and plotting

The package provides simple ways to define discrete densities, for instance a dice:

from probability_calculator.density import Dice

density = Dice(6) # initialize a fair dice with 6 sides
fig, ax = density.plot() # plot the density using matplotlib

For the general case, the class DiscreteDensity can be used:

from probability_calculator.density import DiscreteDensity

density = DiscreteDensity(outcomes=[
    { "value": 0, "prob": 0.2 },
    { "value": 1, "prob": 0.3 },
    { "value": 2.5, "prob": 0.1 },
    { "value": 3, "prob": 0.4 },
]) # initialize a discrete density with 4 different outcomes
fig, ax = density.plot() # plot the density using matplotlib

combine discrete densities

The discrete density of throwing a dice two times can be modelled by multiplying the density with itself:

densityForOneThrow = Dice(6)
densitySumOfTwoThrows = densityForOneThrow * densityForOneThrow # same as densityForOneThrow**2
fig, ax = densitySumOfTwoThrows.plot()

Note that the operations on densities are defined in a way to comply with operations of probability-generating functions. This means multiplication of densities return the density of the sum of the two underlying random variables

Limitations

Continuous densities are not supported at the moment.

Multiplying a lot of densities might get stuck due to a lot of possible outcomes. In general, multiplying 10 densities with 10 outcomes each lead to $10^{10}$ outcomes. However, simple cases like the dice work, Dice(10)**10 is no problem.

Contributing

We greatly appreciate fixes and new features for probability_calculator. All contributions to this project should be sent as pull requests on github.

License

Apache License 2.0