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.
Standard hidden Markov model (HMM) and the more general dynamic Bayesian network (DBN) models assume stationarity of state transition distribution. However, this assumption does not hold for many real life events of interest. In this paper, we propose a new time sequence model that extends HMM to time varying scenario. The time varying property is realized in our model by explicitly allowing the change...
This paper shows how the random sampling, M-estimators, random walk can be combined to create a consistent sampler for generic models in problems that are difficult due to outliers and multimodality of the solution. Our method contains three major steps: (1) finding the local peaks of the selected robust cost function (M-estimator) using seeds from random sampling of minimal configurations, (2) constructing...
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.