Classical Machine Learning
Combine the sensor's clues
BOTIE
Examples, experiments and workspace controls
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.
Context for this challenge
Multiply the observed-sign probabilities across each row, keeping faulty and working rows separate.
Botie has several observed signs. Each table row gives their probabilities under one cause: faulty first, working second.
Inspect the component calculation
Multiply conditional probabilities in each row( Conditional probability rows: Probabilities of observed signs by cause )
Examples and results
- Probabilities of observed signs by cause
0.8 0.7 0.1 0.2 - Required result
- 00.56
- 10.02
- Probabilities of observed signs by cause
0.5 0.4 0.5 0.4 - Required result
- 00.2
- 10.2
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
The table already describes observed outcomes. We are composing an inference calculation, not estimating rates or guaranteeing probability calibration. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.
Your last tested pieces can start the full construction. An existing draft there keeps its own pieces.
One sign offers no distinction between causes
This check uses the pieces from “Weight each cause's likelihood and normalise”.
BOTIE
- Probabilities of observed signs by cause
0.6 0.5 0.2 0.5 - Faulty and working priors
- 00.1
- 10.9
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 assumption allows the sign probabilities to be multiplied within a row, and how could dependent signs distort the result?
A probability calculation inherits the dependence assumptions used to construct its likelihood.
The table already describes observed outcomes. We are composing an inference calculation, not estimating rates or guaranteeing probability calibration.
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