ANC Workshop: David Schnoerr, Chair: Edward Wallace
What 


When 
Jul 04, 2017 from 11:00 AM to 12:00 PM 
Where  IF 4.31/4.33 
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Joint work with Botond Cseke, Ramon Grima and Guido Sanguinetti.
Approximating firstpassage time distributions via sequential Bayesian computation
Many systems in nature consist of stochastically interacting agents or particles. Stochastic processes have been widely used to model such systems, yet they are notoriously difficult to analyse. We consider the problem of computing firstpassage times for Markov jump processes, that is, the time it takes a process to first cross a certain threshold. I will show that this important class of generally intractable problems can be exactly recast in terms of a Bayesian inference problem by introducing auxiliary observations. This leads us to derive an efficient approximation scheme to compute firstpassage time distributions by solving a small, closed set of ordinary differential equations. The method is accurate and orders of magnitude faster than existing approaches, enabling hitherto computationally prohibitive tasks such as sensitivity analysis. We apply it to an epidemic model, an entrained oscillator and a trimerisation process, and show good agreement with exact stochastic simulations.