Laplace based approximate posterior inference for differential equation models
Ordinary differential equations are arguably the most popular and useful mathematical tool for describing physical and biological processes in the real world. Often, these physical and biological processes are observed with errors, in which case the most natural way to model such data is via regress...
Saved in:
Main Authors: | Dass, S.C., Lee, J., Lee, K., Park, J. |
---|---|
Format: | Article |
Published: |
Springer New York LLC
2017
|
Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961615481&doi=10.1007%2fs11222-016-9647-0&partnerID=40&md5=d758663601ac280ee350e0e7a75f3a05 http://eprints.utp.edu.my/19515/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bayesian inference using two-stage Laplace approximation for differential equation models
by: Dass, S.C., et al.
Published: (2016) -
Inference for differential equation models using relaxation via dynamical systems
by: Lee, K., et al.
Published: (2018) -
Inference for differential equation models using relaxation via dynamical systems
by: Lee, K., et al.
Published: (2018) -
Laplace Transform With Modified Analytical Approximate Methods For Fractional Differential Equations
by: Jaber, Hailat Ibrahim Yousef
Published: (2022) -
Some remarks on the Sumudu and Laplace transforms and applications to differential equations
by: Kilicman, Adem, et al.
Published: (2012)