← All workshopsRead Planning for More Than One Possible Outcome

Probabilistic Models and Uncertainty

Average over possible damage

BOTIE

Multiply matching outcomes and probabilities, then add the contributions.
Ready to test
0 passed · 0 failed · 2 not run

An empty floor. Start with the information your machine needs.

Received result

Your answer will arrive here.

Examples, experiments and workspace controls
Live values

Connect a tool's inputs to see what it produces. You can try a branch before connecting it to Result.

Try your own inputs

Use a copy of the example to try your own data. Keep the problem's assumptions in mind, and predict the effect of a change before checking the output. You can also add another branch to compare two ways of doing the calculation.

Start at an output arrow, then click or drag to an input. Drag empty space to pan. Delete removes a selection. Ctrl/Cmd+Z undoes a change.
Context for this challenge

Multiply matching outcomes and probabilities, then add the contributions.

Botie must plan for damage counts that have different chances of occurring. A plain average would give an unlikely outcome too much influence.

Keep each count beside its probability and combine their weighted contributions.

Examples and results
Possible counts
  1. 00
  2. 11
  3. 22
Probabilities
  1. 00.5
  2. 10.3
  3. 20.2
Required result
0.7
Possible counts
  1. 00
  2. 12
Probabilities
  1. 00
  2. 11
Required result
2
Try a fresh construction

This clears this draft, including its undo history. Earlier successful attempts stay recorded. Build again without opening hints to record an independent attempt.

Stuck? Inspect a worked construction

The tools below form one possible solution. You can assemble it, follow its signals, and undo that change to return to your own construction.

Assumptions and test precision

Probabilities must sum to one. This average is not the most likely outcome. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.

A rare badly damaged shipment

This check uses the pieces from “Weight each possible damage count”.

BOTIE

Most shipments have no damaged items, but a few lose eight. Return the expected damaged-item count using every possible outcome and its probability.
Possible counts
  1. 00
  2. 11
  3. 28
Probabilities
  1. 00.8
  2. 10.15
  3. 20.05

Use a number, text, or an array matching the requested result. For a matrix, put each row in brackets. Saving your prediction runs your current construction on these inputs and checks the earlier examples too.

Build a construction in the workspace above first. This case will test those pieces.

What information about the damage distribution is lost when you report only its expectation?

Keep the spread and possible extremes available when the consequences of rare outcomes matter.

Probabilities must sum to one. This average is not the most likely outcome.

Work through the Python challenges →
Continue the courseUpdating an Unknown Failure Rate →
← Repair the Night-Shift Model Without Breaking the Day Shift