Gaussian filtering and variational approximations for Bayesian smoothing in continuous-discrete stochastic dynamic systems
The Bayesian smoothing equations are generally intractable for systems described by
nonlinear stochastic differential equations and discrete-time measurements. Gaussian …
nonlinear stochastic differential equations and discrete-time measurements. Gaussian …
Estimating parameters in stochastic systems: A variational Bayesian approach
This work is concerned with approximate inference in dynamical systems, from a variational
Bayesian perspective. When modelling real world dynamical systems, stochastic differential …
Bayesian perspective. When modelling real world dynamical systems, stochastic differential …
Bayesian equation selection on sparse data for discovery of stochastic dynamical systems
Often the underlying system of differential equations driving a stochastic dynamical system is
assumed to be known, with inference conditioned on this assumption. We present a …
assumed to be known, with inference conditioned on this assumption. We present a …
Nonparametric model reconstruction for stochastic differential equations from discretely observed time-series data
J Ohkubo - Physical Review E—Statistical, Nonlinear, and Soft …, 2011 - APS
A scheme is developed for estimating state-dependent drift and diffusion coefficients in a
stochastic differential equation from time-series data. The scheme does not require to …
stochastic differential equation from time-series data. The scheme does not require to …
Duality-based calculations for transition probabilities in stochastic chemical reactions
J Ohkubo - Physical Review E, 2017 - APS
An idea for evaluating transition probabilities in chemical reaction systems is proposed,
which is efficient for repeated calculations with various rate constants. The idea is based on …
which is efficient for repeated calculations with various rate constants. The idea is based on …
Approximate inference for state-space models
MC Higgs - 2011 - discovery.ucl.ac.uk
This thesis is concerned with state estimation in partially observed diffusion processes with
discrete time observations. This problem can be solved exactly in a Bayesian framework, up …
discrete time observations. This problem can be solved exactly in a Bayesian framework, up …
Approximate Bayesian techniques for inference in stochastic dynamical systems
MD Vrettas - 2010 - publications.aston.ac.uk
This thesis is concerned with approximate inference in dynamical systems, from a variational
Bayesian perspective. When modelling real world dynamical systems, stochastic differential …
Bayesian perspective. When modelling real world dynamical systems, stochastic differential …
[CITATION][C] Estimating parameters in stochastic systems: A variational Bayesian
MD Vrettas, D Cornforda, M Opperb