Probability from first principles
Count outcomes, condition on what you know and handle draws without replacement.
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Short card lessons in the maths quant interviews and technical degrees share: an idea, an example, a common mistake, a quick check.
Count outcomes, condition on what you know and handle draws without replacement.
Turn a counting question into a product, a choice or a correction.
Turn new evidence into a new probability by counting cases, and keep base rates in view.
Why quotes sit where they sit: edge, inventory, adverse selection and arbitrage.
Read variables together: joint tables, the multivariate normal, covariance matrices, and when a correlation means a cause.
Hidden states, random arrivals, random paths and fair games: the models behind regime, flow and risk questions.
Estimate what you cannot integrate, attach an honest error bar, and know when each shortcut leans the wrong way.
Fit parameters by likelihood, know the limit on their precision, test them, and shrink them when the sample is small.
Go past Bayes' rule: share strength between groups, update in closed form, choose actions by expected loss and predict what comes next.
Model what a series remembers, tell a level from its changes, and track a hidden state through noise.
Measure uncertainty in bits, price a wrong model, detect dependence that correlation misses, and use entropy to choose tests and laws.
Measure tail losses, price options without a forecast, read what risk-neutral weights mean, and test a forecast honestly.
Four reusable moves for hard questions: rank distributions, split into cases, count with indicators and bound the tails.
The few ideas behind most interview puzzles: invariants and symmetry, parity and pigeonhole, games solved backwards, and working back from the end.
Derivatives, integrals, series, determinants, eigenvalues and linear systems: the university maths that quant tests and technical exams still ask.