The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
When approximating one probability density with another density, it is desirable to minimize the information loss of the approximation as quantified by, e.g., the Kullback-Leibler divergence (KLD). It has been known for some time that in the case of the Gaussian distribution, matching the first two moments of the original density yields the optimal approximation in terms of minimizing the KLD. In...
Correlated uncertain angular quantities can be modeled using the bivariate wrapped normal distribution. In this paper, we focus on the problem of estimating the distribution's parameters from a given set of samples. For this purpose, we propose several new parameter estimation methods and compare them to estimation techniques found in literature. All methods are thoroughly evaluated in simulations...
We introduce a novel probability distribution on the group of rigid motions SE(2) and we refer to this distribution as the partially wrapped normal distribution. Describing probabilities on SE(2) is of interest in a wide range of applications, for example, robotics, autonomous vehicles, or maritime navigation. We derive some important properties of this novel distribution and propose an estimation...
In this paper, we address the problem of developing computationally efficient recursive estimators on the periodic domain of orientations using the Bingham distribution. The Bingham distribution is defined directly on the unit hypersphere. As such, it is able to describe both large and small uncertainties in a unified framework. In order to tackle the challenging computation of the normalization constant,...
In this paper, a novel distance-based density estimation method is proposed, which considers the overall density function in the goodness-of-fit. In detail, the parameters of Gaussian mixture densities are estimated from samples, based on the distance of the cumulative distributions over the entire state space. Due to the ambiguous definition of the standard multivariate cumulative distribution, the...
In this paper, the first algorithm for learning hybrid Bayesian Networks with Gaussian mixture and Dirac mixture conditional densities from data given their structure is presented. The mixture densities to be learned allow for nonlinear dependencies between the variables and exact closed-form inference. For learning the network's parameters, an incremental gradient ascent algorithm is derived. Analytic...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.