Graphical Models for Visual Object Recognition and Tracking Export

@phdthesis{citeulike:1815514, author = {Sudderth, Erik}, citeulike-article-id = {1815514}, citeulike-linkout-0 = {http://ssg.mit.edu/~esuddert/papers/sudderthPhD.pdf}, comment = {p63 MAP parameter estimate \theta ^ = arg max\_{\theta} p( \theta | y, \lambda) = arg max\_{\theta} p( \theta | \lambda) \int\_{X} p(x,y | \theta) dx ... marginal likelihood p(y|\lambda ) = \int\_{X} \int\_{\Theta} p(x,y|\theta) p(\theta | \lambda) d\theta dx}, keywords = {dirichlet, hierarchicaltopicmodels, mlgeneral}, month = {May}, posted-at = {2007-10-24 15:10:45}, priority = {2}, school = {Massachusetts Institute of Technology}, title = {Graphical Models for Visual Object Recognition and Tracking}, url = {http://ssg.mit.edu/~esuddert/papers/sudderthPhD.pdf}, year = {2006} }

TY - THES ID - citeulike:1815514 L3 - citeulike-article-id:1815514 TI - Graphical Models for Visual Object Recognition and Tracking PB - Massachusetts Institute of Technology KW - dirichlet KW - hierarchicaltopicmodels KW - mlgeneral N1 - p63 MAP parameter estimate \theta ^ = arg max_{\theta} p( \theta | y, \lambda) = arg max_{\theta} p( \theta | \lambda) \int_{X} p(x,y | \theta) dx ... marginal likelihood p(y|\lambda ) = \int_{X} \int_{\Theta} p(x,y|\theta) p(\theta | \lambda) d\theta dx AU - Sudderth, Erik PY - 2006/05// UR - http://ssg.mit.edu/~esuddert/papers/sudderthPhD.pdf ER -