Measures of distinguishability between stochastic processes
| Autor: | Chengran Yang, Mile Gu, Felix C. Binder, Thomas J. Elliott |
|---|---|
| Přispěvatelé: | School of Physical and Mathematical Sciences, Complexity Institute |
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Quantum Physics Stochastic Processes Statistical Mechanics (cond-mat.stat-mech) Computer science business.industry Process (engineering) Stochastic process Mathematical finance Information Theory FOS: Physical sciences Usability 01 natural sciences Measure (mathematics) 010305 fluids & plasmas Task (project management) Physics [Science] 0103 physical sciences 010306 general physics Divergence (statistics) business Set (psychology) Quantum Physics (quant-ph) Condensed Matter - Statistical Mechanics |
| Zdroj: | Physical Review E |
| DOI: | 10.48550/arxiv.1909.08366 |
| Popis: | Quantifying how distinguishable two stochastic processes are is at the heart of many fields, such as machine learning and quantitative finance. While several measures have been proposed for this task, none have universal applicability and ease of use. In this article, we suggest a set of requirements for a well-behaved measure of process distinguishability. Moreover, we propose a family of measures, called divergence rates, that satisfy all of these requirements. Focusing on a particular member of this family—the coemission divergence rate—we show that it can be computed efficiently, behaves qualitatively similar to other commonly used measures in their regimes of applicability, and remains well behaved in scenarios where other measures break down. Ministry of Education (MOE) National Research Foundation (NRF) Published version This research is supported by the National Research Foundation (NRF). Singapore, under its NRFF Fellow programme (Award No. NRF-NRFF2016-02), the Lee Kuan Yew Endowment Fund (Postdoctoral Fellowship), Singapore Ministry of Education Tier 1 Grants No. MOE2017-T1-002-043 and No FQXi-RFP-1809 from the Foundational Questions Institute and Fetzer Franklin Fund (a donor-advised fund of Silicon Valley Community Foundation). F.C.B. acknowledges funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska Curie Grant Agreement No. 801110 and the Austrian Federal Ministry of Education, Science, and Research (BMBWF). T.J.E., C.Y., and F.C.B. thank the Centre for Quantum Technologies for their hospitality. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. |
| Databáze: | OpenAIRE |
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