A distribution has a property named for this person with respect to a graph if and only if it factorises over its cliques. For 10 points each:
[10m] Name this person who names a property in which the future evolution of a random process is independent of its history, conditional on the present state. Transition probabilities define stochastic processes named for this person.
ANSWER: Andrey Markov [accept Markov property or Markov process or Markov chain]
[10h] The strong Markov property is used to prove the reflection principle for this mathematical object, a continuous Gaussian process with quadratic variation equal to t.
ANSWER: Wiener process [or Brownian motion]
[10e] Wiener processes frequently appear in “stochastic” versions of these equations. These equations, which relate a function to its rates of change, also come in “partial” and “ordinary” types.
ANSWER: differential equations [or DEs; accept stochastic differential equations or partial differential equations; accept SDEs or PDEs; prompt on descriptions like equations with derivatives]
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