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