Non-factorised variational inference in dynamical systems
We focus on variational inference in dynamical systems where the discrete time transition
function (or evolution rule) is modelled by a Gaussian process. The dominant approach so
far has been to use a factorised posterior distribution, decoupling the transition function from
the system states. This is not exact in general and can lead to an overconfident posterior
over the transition function as well as an overestimation of the intrinsic stochasticity of the
system (process noise). We propose a new method that addresses these issues and incurs …
function (or evolution rule) is modelled by a Gaussian process. The dominant approach so
far has been to use a factorised posterior distribution, decoupling the transition function from
the system states. This is not exact in general and can lead to an overconfident posterior
over the transition function as well as an overestimation of the intrinsic stochasticity of the
system (process noise). We propose a new method that addresses these issues and incurs …
Non-Factorised Variational Inference in Dynamical Systems
A Davide Ialongo, M van der Wilk, J Hensman… - arXiv e …, 2018 - ui.adsabs.harvard.edu
We focus on variational inference in dynamical systems where the discrete time transition
function (or evolution rule) is modelled by a Gaussian process. The dominant approach so
far has been to use a factorised posterior distribution, decoupling the transition function from
the system states. This is not exact in general and can lead to an overconfident posterior
over the transition function as well as an overestimation of the intrinsic stochasticity of the
system (process noise). We propose a new method that addresses these issues and incurs …
function (or evolution rule) is modelled by a Gaussian process. The dominant approach so
far has been to use a factorised posterior distribution, decoupling the transition function from
the system states. This is not exact in general and can lead to an overconfident posterior
over the transition function as well as an overestimation of the intrinsic stochasticity of the
system (process noise). We propose a new method that addresses these issues and incurs …
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