Name
Abstract | ||
|---|---|---|
We introduce a family of implicit probabilistic integrators for initial value problems (IVPs) taking as a starting point the multistep Adams--Moulton method. The implicit construction allows for dynamic feedback from the forthcoming time-step, by contrast with previous probabilistic integrators, all of which are based on explicit methods. We begin with a concise survey of the rapidly-expanding field of probabilistic ODE solvers. We then introduce our method, which builds on and adapts the work of Conrad et al. (2016) and Teymur et al. (2016), and provide a rigorous proof of its well-definedness and convergence. We discuss the problem of the calibration of such integrators and suggest one approach. We give an illustrative example highlighting the effect of the use of probabilistic integrators -- including our new method -- in the setting of parameter inference within an inverse problem. |
Year | Venue | Keywords |
|---|---|---|
2018 | neural information processing systems | rigorous proof,starting point,inverse problem |
Field | DocType | ISSN |
Econometrics,Convergence (routing),Applied mathematics,Inference,Integrator,Initial value problem,Inverse problem,Probabilistic logic,Ode,Mathematics | Conference | Advances in Neural Information Processing Systems 31 (2018) pp.
7244-7253 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Onur Teymur | 1 | 4 | 1.47 |
| Lie, Han Cheng | 2 | 0 | 0.34 |
| Tim Sullivan | 3 | 12 | 1.74 |
| Ben Calderhead | 4 | 98 | 9.08 |