Learning Dynamic Bayesian Networks

By Zoubin Ghahramani. 1997

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Citations in the corpus

Learning deep dynamical models from image pixels (2014, neutral): “, subspace methods (Van Overschee and De Moor, 1996), expectation maximization (Shumway and Stoffer, 1982; Ghahramani, 1998; Gibson and Ninness, 2005) and prediction-error methods (Ljung, 1999).”
Near-optimal Reinforcement Learning in Factored MDPs (2014, background): “M L ] 1 5 M ar 2 Any learning algorithm over Markov decision processes (MDPs) will have worst-case regret Ω( √”
Model-based Utility Functions (2011, background): “And there are practical algorithms and approaches for learning MDPs and DBNs (Baum et al, 1970; Sutton and Barto, 1998; Alamino and Nestor, 2006; Hutter 2009a; Hutter 2009b; Gisslén et al, 2011).”
Granger Causality: A Review and Recent Advances (2021, result): “This representation is similar to that in dynamic Bayesian networks (Ghahramani, 1997).”
Recognition of group activities using dynamic probabilistic networks (2003, methodology): “Dynamic probabilistic networks and in particular Dynamic Bayesian Networks (DBNs) are BBNs that have been extended to model time series data [ 6 , 9]. More specifically, hidden nodes have been introduced in the topology of DBNs to represent hidden temporal states.”
Machine learning for detection and diagnosis of disease. (2006, background): “Variants of Bayesian networks include dynamic Bayesian networks (94), useful for constructing generative models of ordered sequential data (e.”
Spreadsheet simulation (2004, methodology): “For tiers where a black-box building block is considered sufficient, Bayesian-network learning algorithms (Ghahramani 1998) are applied, to derive a response time function for each request type, depending on the state of the modeled component.”
Dynamic Bayesian Networks for Audio-Visual Speech Recognition (2002, methodology): “The audio-visual models used in existing AVSR systems, as well as the audio-visual models discussed in this paper , are special cases of dynamic Bayesian networks (DBN) [5] [21] [22].”
Adaptive computation and machine learning (1998, background): “In the speech domain, for instance, one chain can represent audio information and the other video information about lip configuration [203, 205].”
Finding your friends and following them to where you are (2012, background): “Since equation 7 is computationally intractable for sizable domains, we simplify by optimizing its lower bound instead, similar to [13].”
On the Convexity of Latent Social Network Inference (2010, methodology): “Recently, grap hic l Lasso methods [25, 21, 6, 19] for static sparse graph estimation and extensions to time evolv ing graphical models [1, 8, 22] have been proposed with lots of success.”
ARMA Time-Series Modeling with Graphical Models (2004, methodology): “…algorithms for parameter estimation (including estimation in the presence of missing data), for prediction, and for cross prediction—the use of one time series to help predict another time series (e.g., Ghahramani, 1998; Meek, Chickering, and Heckerman, 2002; Bach and Jordan, 2004).”
MEBN: A language for first-order Bayesian knowledge bases (2008, background): “One common type of recursive graphical model is a dynamic Bayesian network [29,57].”
Learning Bayesian networks: approaches and issues (2011, methodology): “This is generalized to DBNs and an analysis is done over many different specializations of DBNs (Ghahramani, 1998).”