Strongly constrained stochastic processes: the multi-ends Brownian bridge

Abstract

In a recent article, Krapivsky and Redner (J. Stat. Mech. 093208 (2018)) established that the distribution of the first hitting times for a diffusing particle subject to hitting an absorber is independent of the direction of the external flow field. In the present paper, we build upon this observation and investigate when the conditioning on the diffusion leads to a process that is totally independent of the flow field. For this purpose, we adopt the Langevin approach, or more formally the theory of conditioned stochastic differential equations. This technique allows us to derive a large variety of stochastic processes: in particular, we introduce a new kind of Brownian bridge ending at two different final points and calculate its fundamental probabilities. This method is also very well suited for generating statistically independent paths. Numerical simulations illustrate our findings.

26 pages, 6 figures

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• Joint distribution of two Local Times for diffusion processes with the application to the construction of various conditioned processes
• First-passage time of a Brownian motion: two unexpected journeys
• Markov spin models for image generation : explicit large deviations with respect to the number of pixels
• Conditioning the tanh-drift process on first-passage times: Exact drifts, bridges, and process equivalences
• Conditioning two diffusion processes with respect to their first-encounter properties