Preprint Case Report Version 1 Preserved in Portico This version is not peer-reviewed

Forecasting with Importance-Sampling and Path-Integrals

Version 1 : Received: 16 September 2020 / Approved: 17 September 2020 / Online: 17 September 2020 (07:53:32 CEST)
Version 2 : Received: 7 October 2020 / Approved: 8 October 2020 / Online: 8 October 2020 (13:07:42 CEST)
Version 3 : Received: 10 October 2020 / Approved: 12 October 2020 / Online: 12 October 2020 (15:15:40 CEST)

How to cite: Ingber, L. Forecasting with Importance-Sampling and Path-Integrals. Preprints 2020, 2020090385. Ingber, L. Forecasting with Importance-Sampling and Path-Integrals. Preprints 2020, 2020090385.


Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are described as being useful to detail such systems. Method: ASA and PATHINT/PATHTREE have been demonstrated as being effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Results: Not only can selected systems in these three disciplines be aptly modeled, but results of detailed calculations have led to new results and insights not previously obtained. Conclusion: While optimization and path-integral algorithms are now quite well-known (at least to many scientists), these applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

Supplementary and Associated Material Lester Ingber's Archive


path integral; importance sampling; financial options; combat analysis


Physical Sciences, Applied Physics

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