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Variational Gaussian Process State-Space Models

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Investigating variational Gaussian process state-space models with Gaussian likelihood We have demonstrated a principled method to infer the properties of dy-namical systems, the doubly

Laplace Approximated Gaussian Process State-Space Models

What is Gaussian Process? [Intuitive Explaination] | by Joanna | Geek ...

Linear Gaussian State-Space Models are widely used and a Bayesian treatment of parameters is therefore of considerable interest. The approximate Variational Bayesian method applied to

The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where unknown transition and/or measurement mappings are described by GPs. Most ABSTRACT State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian

Differential equations are important mechanistic models that are integral to many scientific and engineering applications. With the abundance of available data there has been a

Gaussian process (GP) regression with 1D inputs can often be performed in linear time via a stochastic differential equation formulation. However, for non-Gaussian likelihoods,

Physics-Informed Variational State-Space Gaussian Processes

We propose a new variational inference algorithm for learning in Gaussian Process State-Space Models (GPSSMs). Our algorithm enables learning of unstable and partially

Abstract Differential equations are important mechanistic models that are integral to many scientific and engineering applications. With the abundance of available data there has been a Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification.

Abstract We propose a new variational inference algorithm for learning in Gaussian Process State-Space Models (GPSSMs). Our algorithm enables learning of unstable and partially

Authors Oliver Hamelijnck, Arno Solin, Theodoros Damoulas Abstract Differential equations are important mechanistic models that are integral to many scientific and engineering applications.

Gaussian Process State-Space Models with Time-Varying

Gaussian process state-space models (GPSSMs) provide a principled and flexible approach to modeling the dynamics of a latent state, which is observed at discrete-time points via a

We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its Abstract Gaussian process state-space models (GPSSMs) provide a principled and flexible approach to mod-eling the dynamics of a latent state, which is observed at discrete

We propose time-varying Gaussian process state-space models (TVGPSSM) whose hyper-parameters vary with time. The models have the ability to estimate time-varying functions and Finally, we propose a new inference approach for Gaussian process state-space models that trades off the properties of state-of-the-art methods in this field. By combining variational

State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed Gaussian Latent Gaussian process (GP) models are widely used in neuroscience to uncover hidden state evolutions from sequential observations, mainly in neural activity recordings.

Abstract—Gaussian process state-space models (GPSSMs) of-fer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. Abstract We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP Abstract Gaussian process state-space models (GPSSMs) provide a principled and flexible approach to mod-eling the dynamics of a latent state, which is observed at discrete-time points

Abstract Gaussian process state-space models (GPSSMs) provide a principled and flexible approach to mod-eling the dynamics of a latent state, which is observed at discrete-time points Abstract Gaussian process state-space models (GPSSMs) provide a principled and flexible approach to modeling the dynamics of a latent state, which is observed at discrete-time points In summary, the key contributions of this paper are: Combining gradient-based and sample-based inference for efficient learning of nonlinear Gaussian process state-space models; Tractable

Abstract Gaussian process state-space models describe time series data in a probabilistic and non-parametric manner using a Gaussian process transition func-tion. As inference is Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan, Edwin V. Bonilla, Terence J. O’Kane and Scott A. Sisson

Abstract The Gaussian process state space model (GPSSM) is a non-linear dynamical sys-tem, where unknown transition and/or measurement mappings are described by GPs. Most Abstract State-space models have been successfully used for more than fifty years in differ-ent areas of science and engineering. We present a procedure for efficient varia-tional Bayesian Abstract State-space models have been successfully used for more than fifty years in differ-ent areas of science and engineering. We present a procedure for efficient varia-tional Bayesian

Abstract Gaussian process state-space models (GPSSMs) provide a principled and flexible approach to mod-eling the dynamics of a latent state, which is observed at discrete-time points

In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the models, the

Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate