New Zealand’s diadromous and (2009)
J. R. Leathwick, J. Elith, W. L. Chadderton, D. Rowe, T. Hastie
Dispersal, disturbance and the
Diadromous Fish Leathwick, J. R. Leathwick, D. Rowe, J. Richardson, J. Elith, T. Hastie
This paper deals with these observations as records of occurrence, although strictly speaking they are records of capture. We recognise the potential for confounding between detectability, capture...
Presence-only data and the EM algorithm (2008)
Ward Hastie Barry, G. Ward, T. Hastie, S. Barry, J. Elith, J. R. Leathwick
this paper is based on strict assumptions about the sampling mechanisms. In particular, we assume that the observed presences in the presenceonly sample are taken at random from all locations, at a...
Description Some functions for sample classification in microarrays (2008)
T. Hastie, R. Tibshirani, Balasubramanian Narasimhan, Gil Chu, Maintainer Rob Tibshirani, Lazyload False, ...
R topics documented: khan............................................. 2 pamr.adaptthresh...................................... 3 pamr.batchadjust...................................... 4...
D. Ormoneit, H. Sidenbladh, M. J. Black, T. Hastie, D. J. Fleet
We present a method for the modeling and tracking of human motion using a sequence of 2D video images. Our analysis is divided in two parts: statistical learning and Bayesian tracking. First, we...
Application of microarray analyses to identify genes involved in radiation-induced fibrosis (2005)
Rødningen, OK, Alsner, J, Hastie, T, Overgaard, J, Børresen-Dale, A-L
No abstract available.
Generalized Additive Models. (2002)
Likelihood based regression models, such as the normal linear regression model and the linear logistic model, assume a linear (or some other parametric) form for the covariate effects. The authors...
Learning and tracking cyclic human motion (2001)
D. Ormoneit, H. Sidenbladh, M. J. Black, T. Hastie
We present methods for learning and tracking human motion in video. We estimate a statistical model of typical activities from a large set of 3D periodic human motion data by segmenting these data...
Learning and tracking cyclic human motion (2001)
D. Ormoneit, H. Sidenbladh, M. J. Black, T. Hastie
We present methods for learning and tracking human motion in video. We estimate a statistical model of typical activities from a large set of 3D periodic human motion data by segmenting these data...
Learning and Tracking Cyclic Human Motion (2001)
D. Ormoneit, H. Sidenbladh, M. J. Black, T. Hastie
We present methods for learning and tracking human motion in video. We estimate a statistical model of typical activities from a large set of 3D periodic human motion data by segmenting these data...
Learning and Tracking Cyclic Human (2001)
Motion Ormoneit Stanford, D. Ormoneit, M. J. Black, H. Sidenbladh, T. Hastie
We present methods for learning and tracking human motion in video. We estimate a statistical model of typical activities from a large set of 3D periodic human motion data by segmenting these data...
Learning and tracking human motion using functional analysis, submitted (2000)
D. Ormoneit, H. Sidenbladh, M. J. Black, T. Hastie, D. Fleet
We present a method for the modeling and tracking of human motion using a sequence of 2D video images. Our analysis is divided in two parts: statistical learning and Bayesian tracking. First, we...
Proc. IEEE Workshop on Human Modeling, Analysis and Synthesis, Hilton Head, SC, June 2000. c (2000)
Ieee Learning And, D. Ormoneit, H. Sidenbladh Ý, T. Hastie
We present a method for the modeling and tracking of human motion using a sequence of 2D video images.
D. Ormoneit, M. J. Black, H. Sidenbladh, T. Hastie
We present methods for learning and tracking human motion in video. We estimate a statistical model of typical activities from a large set of 3D periodic human motion data by segmenting these data...