Probability, conditioning and expectation
Part of the Quant learning and recruiting path. Read the explanation and example, attempt the task, then open the self-check.
Know what to learn for your role.
From foundations to a work sample, with sources, tasks and self-checks.
Understand
Define outcomes and events before computing probabilities. Conditioning changes the information set. Expectation weights outcomes by probabilities; it does not guarantee a realised result.
Worked example
A fair die has expectation 3.5. Conditional on an even result, expectation is (2+4+6)/3 = 4.
Examples and numbers are educational, not employer questions, live market data or hiring criteria.
Practise and keep your output
Compute P(sum=7) for two fair dice, then conditional on the first being 3.
Keep inputs and sources, assumptions, steps, results and uncertainties in your own document. Attempt independently before checking below.
After attempting: answer and self-check
Both probabilities are 1/6, but the first uses 6 of 36 outcomes and the second one of six conditional outcomes.
Official channels and further reading
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- Jane Street · Careers ↗
Separate research, trading and development, then confirm location and eligibility.
- Jane Street · Interviewing ↗
Choose official interview preparation for the relevant role.
- Python · Official tutorial ↗
Programming-language reference. Complete this line’s basic exercises alongside it.