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Generative models for discrete data

. Machine Learning: A Probabilistic Approach, MIT Press, Cambridge, MA, USA, (2012)

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Machine learning : a probabilistic perspective. MIT Press, Cambridge, Mass. u.a., (2013)Bayesian statistics. Machine Learning: A Probabilistic Approach, MIT Press, Cambridge, MA, USA, (2012)Markov and Hidden Markov Models. Machine Learning: A Probabilistic Approach, MIT Press, Cambridge, MA, USA, (2012)Generative models for discrete data. Machine Learning: A Probabilistic Approach, MIT Press, Cambridge, MA, USA, (2012)Modeling changing dependency structure in multivariate time series., and . ICML, volume 227 of ACM International Conference Proceeding Series, page 1055-1062. ACM, (2007)The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction., , , , and . CoRR, (2019)Accelerated training of conditional random fields with stochastic gradient methods., , , and . ICML, volume 148 of ACM International Conference Proceeding Series, page 969-976. ACM, (2006)Vision-Based Speaker Detection Using Bayesian Networks., , and . CVPR, page 2110-2116. IEEE Computer Society, (1999)Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification., , and . CRV, page 371-378. IEEE Computer Society, (2011)Object Detection and Localization Using Local and Global Features., , , and . Toward Category-Level Object Recognition, volume 4170 of Lecture Notes in Computer Science, page 382-400. Springer, (2006)