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Generative models for discrete data

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

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Sharing Visual Features for Multiclass and Multiview Object Detection., , and . IEEE Trans. Pattern Anal. Mach. Intell., 29 (5): 854-869 (2007)Modeling changing dependency structure in multivariate time series., and . ICML, volume 227 of ACM International Conference Proceeding Series, page 1055-1062. ACM, (2007)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)A coupled HMM for audio-visual speech recognition., , , , , and . ICASSP, page 2013-2016. IEEE, (2002)A Hybrid Conditional Random Field for Estimating the Underlying Ground Surface From Airborne LiDAR Data., , , , and . IEEE Trans. Geosci. Remote. Sens., 47 (8-2): 2913-2922 (2009)Bayesian statistics. Machine Learning: A Probabilistic Approach, MIT Press, Cambridge, MA, USA, (2012)Machine learning : a probabilistic perspective. MIT Press, Cambridge, Mass. u.a., (2013)Markov and Hidden Markov Models. Machine Learning: A Probabilistic Approach, MIT Press, Cambridge, MA, USA, (2012)A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables. CoRR, (2013)Generative models for discrete data. Machine Learning: A Probabilistic Approach, MIT Press, Cambridge, MA, USA, (2012)