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ICPR
2010
IEEE
15 years 10 months ago
CDP Mixture Models for Data Clustering
—In Dirichlet process (DP) mixture models, the number of components is implicitly determined by the sampling parameters of Dirichlet process. However, this kind of models usually...
Yangfeng Ji, Tong Lin, Hongbin Zha
VRIPHYS
2010
15 years 1 months ago
A Triangle Bending Constraint Model for Position-Based Dynamics
We present a novel bending model and constraint creation method for position-based dynamics. Our new bending model is introduced as an alternative to the current state-of-the-art ...
Micky Kelager, Sarah Niebe, Kenny Erleben
CHI
2005
ACM
16 years 7 months ago
Supporting efficient development of cognitive models at multiple skill levels: exploring recent advances in constraint-based mod
This paper presents X-PRT, a new cognitive modeling tool supporting activities ranging from interface design to basic cognitive research. X-PRT provides a graphical model developm...
Irene Tollinger, Richard L. Lewis, Michael McCurdy...
SMA
2011
ACM
265views Solid Modeling» more  SMA 2011»
15 years 1 months ago
Modeling individual migraine severity with autoregressive ordered probit models
This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics. We take on the viewpoint of a patient who is i...
Claudia Czado, Anette Heyn, Gernot Müller
NIPS
2001
15 years 8 months ago
Model Based Population Tracking and Automatic Detection of Distribution Changes
Probabilistic mixture models are used for a broad range of data analysis tasks such as clustering, classification, predictive modeling, etc. Due to their inherent probabilistic na...
Igor V. Cadez, Paul S. Bradley