Markov Processes and Control TheoryH. Langer, Volker Nollau |
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applications assume assumption BAD processes belongs Borel measurable boundary bounded Casteren compact condition consider continuous convergence Corollary defined definition denote distribution DMGM dynamic programming E₂ exists exp(at finite formula function gambling models gap diffusion given Hence holds hypotheses idempotent inequality int(Km kernel Lemma lim sup Lin'kov linear Lv s,a M₁ mappings Markov process Markov property Markov strategies martingale Math matrices measurable function Mukherjea non-negative obtain operator optimal stopping P₂ Po(t pointwise probability measure probability space problem proof of Theorem Proposition proved quasi-convergent model random variable respect satisfied Schäl Schrödinger Schrödinger semigroups semigroup Pv(t semimartingales sequence solution space stationary strategies Statist stochastic differential equation stochastic integral subset Sudderth suppose theory unique Univ V₂ vector-valued Wiener process zero δε Δ