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: Applications to COVID-19. Preprints2020, 2020090385. https://doi.org/10.20944/preprints202009.0385.v2
Ingber, L. Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19. Preprints 2020, 2020090385. https://doi.org/10.20944/preprints202009.0385.v2
Ingber, L. Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19. Preprints2020, 2020090385. https://doi.org/10.20944/preprints202009.0385.v2
APA Style
Ingber, L. (2020). Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19. Preprints. https://doi.org/10.20944/preprints202009.0385.v2
Chicago/Turabian Style
Ingber, L. 2020 "Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19" Preprints. https://doi.org/10.20944/preprints202009.0385.v2
Abstract
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. Applications are described for COVID-19. 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Lester Ingber
Commenter's Conflict of Interests: Author