kramersmoyal: Kramers-Moyal coefficients for stochastic processes

Leonardo Rydin Gorjão; Francisco Meirinhos

doi:10.21105/joss.01693

kramersmoyal: Kramers-Moyal coefficients for stochastic processes

Leonardo Rydin Gorjão^*, Francisco Meirinhos

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

kramersmoyal is a python package designed to obtain the Kramers–Moyal coefficients, or conditional moments, from stochastic data of any dimension. It employs kernel density estimations, instead of a histogram approach, to ensure better results for low number of points as well as allowing better fitting of the results.

Original language	English
Pages (from-to)	1693
Journal	Journal of Open Source Software
Volume	4
Issue number	44
DOIs	https://doi.org/10.21105/joss.01693
Publication status	Published - 19 Dec 2019
Externally published	Yes

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10.21105/joss.01693Licence: CC BY

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title = "kramersmoyal: Kramers-Moyal coefficients for stochastic processes",

abstract = "kramersmoyal is a python package designed to obtain the Kramers–Moyal coefficients, or conditional moments, from stochastic data of any dimension. It employs kernel density estimations, instead of a histogram approach, to ensure better results for low number of points as well as allowing better fitting of the results.",

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AB - kramersmoyal is a python package designed to obtain the Kramers–Moyal coefficients, or conditional moments, from stochastic data of any dimension. It employs kernel density estimations, instead of a histogram approach, to ensure better results for low number of points as well as allowing better fitting of the results.

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kramersmoyal: Kramers-Moyal coefficients for stochastic processes

Abstract

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