Mixed-State Models for Nonstationary Multiobject Activities
نویسندگان
چکیده
منابع مشابه
Mixed-State Models for Nonstationary Multiobject Activities
We present a mixed-state space approach for modeling and segmenting human activities. The discrete-valued component of the mixed state represents higher-level behavior while the continuous state models the dynamics within behavioral segments. A basis of behaviors based on generic properties of motion trajectories is chosen to characterize segments of activities. A Viterbi-based algorithm to det...
متن کاملGeneralized state - space models for modeling nonstationary EEG time - series
Contemporary neuroscientific research has access to various techniques for recording time-resolved data relating to human brain activity: electroencephalography (EEG) and magnetoencephalography (MEG) record the electromagnetic fields generated by the brain, while other techniques, such as near-infrared spectroscopy (NIRS) and functional magnetic resonance imaging (fMRI) are sensitive to the loc...
متن کاملInitializing the Kalman Filter for Nonstationary State Space Models
The Kalman filter cannot be used with nonstationary state space models. To circumvent this difficulty, a conditional state space model and a new algorithm, calIed the conditional Kalman filter, can be used. The conditional state space model is obtained by first selecting adequately that part oof the initial state vector which has an unspecified distribution and then conditioning on O. Using the...
متن کاملNonstationary models for shallow lakes
In this paper we study the behaviour of solutions of Navier{Stokes equations with adherence to the bottom and traction by wind at the surface when the aspect ratio = depth=lenght of the domain tends to 0. Precisely we prove that when wind is moderate, a variational solution converges to the solution of a vertical diiusion model. This model is either quasistationary or nonstationary, according t...
متن کاملBayesian Inference for Spatial Beta Generalized Linear Mixed Models
In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2006
ISSN: 1687-6180
DOI: 10.1155/2007/65989