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The result clearly shows that the IMEKF and the NLS-based method are superior to q-IEKF and all three outperform the non-iterative methods.Alexandre Khae Wu Navarro, Jes Frellsen, and Richard E. The Multivariate Generalised von Mises distribution: Inference and applications. Abstract: Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community.
PILCO evaluates policies by planning state-trajectories using a dynamics model.However, PILCO applies policies to the observed state, therefore planning in observation space.We extend PILCO with filtering to instead plan in belief space, consistent with partially observable Markov decisions process (POMDP) planning. Abstract: This paper presents three iterative methods for orientation estimation.covariance functions and methods for automatic relevance determination). Although elegant, the application of GPs is limited by computational and analytical intractabilities that arise when data are sufficiently numerous or when employing non-Gaussian models.Third, we show that the posterior distribution in these models is a m Gv M distribution which enables development of an efficient variational free-energy scheme for performing approximate inference and approximate maximum-likelihood learning. Consequently, a wealth of GP approximation schemes have been developed over the last 15 years to address these key limitations.Many of these schemes employ a small set of pseudo data points to summarise the actual data.
In this paper we develop a new pseudo-point approximation framework using Power Expectation Propagation (Power EP) that unifies a large number of these pseudo-point approximations.
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[ GPs | Clustering | Graphical Models | MCMC | Semi-Supervised | Non-Parametric | Approximations | Bioinformatics | Information Retreival | RL and Control | Time Series | Network Modelling | Active Learning | Neuroscience | Signal Processing | Machine Vision | Machine Hearing | NLP | Deep Learning | Review ] [ Balog | Bauer | Bui | Dziugaite | Ge | Ghahramani | Gu | Hernández-Lobato | Kilbertus | Kok | Li | Lomeli | Matthews | Navarro | Peharz | Rasmussen | Rojas-Carulla | Rowland | Ścibior | Shah | Steinrücken | Rich Turner | Weller ] [ Borgwardt | Bratières | Calliess | Chen | Cunningham | Davies | Deisenroth | Duvenaud | Eaton | Frellsen | Frigola | Van Gael | Gal | Heaukulani | Heller | Hoffman | Houlsby | Huszár | Knowles | Lacoste-Julien | Lloyd | Lopez-Paz | Mc Allister | Mc Hutchon | Mohamed | Orbanz | Ortega | Palla | Quadrianto | Roy | Saatçi | Tobar | Ryan Turner | Snelson | van der Wilk | Williamson | Wilson ] Gaussian processes are non-parametric distributions useful for doing Bayesian inference and learning on unknown functions.
The third method is based on nonlinear least squares (NLS) estimation of the angular velocity which is used to parametrise the orientation.
The results are obtained using Monte Carlo simulations and the comparison is done with the non-iterative EKF and multiplicative EKF (MEKF) as baseline.
They can be used for non-linear regression, time-series modelling, classification, and many other problems., volume 31, Long Beach, California, USA, December 2017.