by Cecilia Aubrun, Jean-Philippe Bouchaud, Michael Benzaquen
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Suggested foundations for optimisation and numerical methods and probability and statistical reasoning. Specialist prerequisites vary by paper.
Read the linear algebra used to define a numerical problem.
Identify the objective, constraints and conditions for an optimum.
Read the distributions and expectations used in probabilistic arguments.
Interpret expectations after information is observed.
Understand identifiability and fitted model parameters.
Separate model error from unstable numerical computation.
Assess uncertainty rather than treating an estimate as a fact.
Move from conditioning and expected values to timed problems with a defensible solution.