Expectation propagation for approximate inference in dynamic Bayesian networks

By Tom Heskes Onno Zoeter. 2002

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Dustin Tran
2. Yoshua Bengio
3. David M. Blei
4. Sergey Levine
5. Ruslan Salakhutdinov
6. Aki Vehtari
7. Balaji Lakshminarayanan
8. Trevor Darrell

Citations in the corpus

• Effective Bayesian Modeling of Groups of Related Count Time Series (2014, positive): “More recently, the expectation propagation (EP) algorithm of Minka (2001), a generalization of ADF, has proved successful in a number of non-linear filtering and smoothing problems (Heskes & Zoeter, 2002; Yu et al., 2006; Deisenroth & Mohamed, 2012).”
• State Space Gaussian Processes with Non-Gaussian Likelihood (2018, methodology): “Unlike full state space EP using forward and backward passes (Heskes & Zoeter, 2002), there is a single-pass variant doing only one forward sweep that is know as assumed density filtering (ADF).”
• Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server (2015, methodology): “In the following we will use “EP” to refer to both EP and power EP for simplicity.”
• Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data (2014, methodology): “Similar to the methods presented by Heskes and Zoeter (2002) and Opper and Winther (2005), they implement a convergent double-loop optimization algorithm, which has the same optima as power EP (Minka, 2004).”