%0 Journal Article %1 tsourtis2020inference %A Tsourtis, A %A Pantazis, Y %A Tsamardinos, I %D 2020 %J arXiv:2012.05055v1 cs.LG 9 Dec 2020 %K mxmcausalpath %R arXiv:2012.05055v1 cs.LG 9 Dec 2020 %T Inference of Stochastic Dynamical Systems from Cross-Sectional Population Data %X Inferring the driving equations of a dynamical system from population or time-course data is important in several scientific fields such as biochemistry, epidemiology, financial mathematics and many others. Despite the existence of algorithms that learn the dynamics from trajectorial measurements there are few attempts to infer the dynamical system straight from population data. In this work, we deduce and then computationally estimate the Fokker-Planck equation which describes the evolution of the population’s probability density, based on stochastic differential equations. Then, following the USDL approach 22, we project the Fokker-Planck equation to a proper set of test functions, transforming it into a linear system of equations. Finally, we apply sparse inference methods to solve the latter system and thus induce the driving forces of the dynamical system. Our approach is illustrated in both synthetic and real data including non-linear, multimodal stochastic differential equations, biochemical reaction networks as well as mass cytometry biological measurements.

@article{tsourtis2020inference, abstract = {Inferring the driving equations of a dynamical system from population or time-course data is important in several scientific fields such as biochemistry, epidemiology, financial mathematics and many others. Despite the existence of algorithms that learn the dynamics from trajectorial measurements there are few attempts to infer the dynamical system straight from population data. In this work, we deduce and then computationally estimate the Fokker-Planck equation which describes the evolution of the population’s probability density, based on stochastic differential equations. Then, following the USDL approach [22], we project the Fokker-Planck equation to a proper set of test functions, transforming it into a linear system of equations. Finally, we apply sparse inference methods to solve the latter system and thus induce the driving forces of the dynamical system. Our approach is illustrated in both synthetic and real data including non-linear, multimodal stochastic differential equations, biochemical reaction networks as well as mass cytometry biological measurements.}, added-at = {2021-03-24T10:32:03.000+0100}, author = {Tsourtis, A and Pantazis, Y and Tsamardinos, I}, biburl = {https://www.bibsonomy.org/bibtex/2f3d7571025e47ab9693c1b8a5876702d/mensxmachina}, doi = {arXiv:2012.05055v1 [cs.LG] 9 Dec 2020}, interhash = {1dd0cba1cddecc67bc714ff55e2fa939}, intrahash = {f3d7571025e47ab9693c1b8a5876702d}, journal = {arXiv:2012.05055v1 [cs.LG] 9 Dec 2020}, keywords = {mxmcausalpath}, timestamp = {2021-03-24T10:32:03.000+0100}, title = {Inference of Stochastic Dynamical Systems from Cross-Sectional Population Data }, year = 2020 }