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Chemins rugueux issus de processus discrets

Abstract : Through the present work, we hope to contribute to extending the domain of applications of rough paths theory by studying the convergence of discrete processes and thus allowing for a new point of view on several issues appearing in the setting of classical stochastic calculus. We study the convergence, first of Markov chains on periodic graphs, then of hidden Markov walks, in rough path topology, and we show that this change of setting allows to bring forward extra information on the limit using the area anomaly, which is invisible in the uniform topology. We want to show that the utility of this object goes beyond the setting of dierential equations. We also show how rough paths can be used to encode the way we embed a discrete process in the space of continuous functions, and that the limits of these embeddings dier precisely by the area anomaly term. We then define the iterated occupation times for a Markov chain and show using iterated sums that they form an underlying combinatorial structure for hidden Markov walks. We then construct rough paths using iterated sums and compare them to the classical construction, which uses iterated integrals, to get two dierent types of rough paths at the limit: the non-geometric and the geometric one respectively. Finally, we illustrate the computation and construction of the area anomaly and we give some extra results on the convergence of iterated sums and occupation times.
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Olga Lopusanschi. Chemins rugueux issus de processus discrets. Probabilités [math.PR]. Sorbonne Université, 2018. Français. ⟨NNT : 2018SORUS074⟩. ⟨tel-02383238⟩

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