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Estimation, Inference & Fermi

MLE, tests, risk measures, filtering, Fermi bounds.

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Concentration inequalities: tail bounds without distribution
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recommend Variance of sums + law of total variance +1 more first
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Fermi estimation: order-of-magnitude from anchored factors
recommended next
Hypothesis testing: assume $H_0$, measure the surprise
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recommend Central Limit Theorem: turn a sum into a Normal +1 more first
Kalman filter: inverse-variance weighting with memory
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recommend Normal: the CLT limit, the workhorse +1 more first
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Entropy, cross-entropy, KL: cost of the wrong model
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Maximum likelihood: write it, log it, differentiate, solve
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recommend Bayes' rule: inverting the conditioning first
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Work rates: reduce everyone to per-unit, then add
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Relative motion: pick a frame, subtract speeds
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VaR and Expected Shortfall: why ES is the coherent risk measure
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recommend Lognormal: multiplicative Normal — stock prices +3 more first
Info-theoretic lower bounds: counting hypotheses vs. outcomes
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recommend Entropy, cross-entropy, KL: cost of the wrong model first
Fisher information: how well can you know your parameter
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recommend Maximum likelihood: write it, log it, differentiate, solve +3 more first