Search Results for author: Alan S. Willsky

Found 9 papers, 1 papers with code

Learning Gaussian Graphical Models with Observed or Latent FVSs

no code implementations NeurIPS 2013 Ying Liu, Alan S. Willsky

Exact inference such as computing the marginal distributions and the partition function has complexity $O(k^{2}n)$ using message-passing algorithms, where k is the size of the FVS, and n is the total number of nodes.

Bayesian Nonparametric Inference of Switching Linear Dynamical Systems

1 code implementation19 Mar 2010 Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky

Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.

Sharing Features among Dynamical Systems with Beta Processes

no code implementations NeurIPS 2009 Emily Fox, Michael. I. Jordan, Erik B. Sudderth, Alan S. Willsky

We propose a Bayesian nonparametric approach to relating multiple time series via a set of latent, dynamical behaviors.

Time Series

A sticky HDP-HMM with application to speaker diarization

no code implementations15 May 2009 Emily B. Fox, Erik B. Sudderth, Michael. I. Jordan, Alan S. Willsky

To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer.

Speaker Diarization

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems

no code implementations NeurIPS 2008 Emily Fox, Erik B. Sudderth, Michael. I. Jordan, Alan S. Willsky

Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.

Loop Series and Bethe Variational Bounds in Attractive Graphical Models

no code implementations NeurIPS 2007 Alan S. Willsky, Erik B. Sudderth, Martin J. Wainwright

Variational methods are frequently used to approximate or bound the partition or likelihood function of a Markov random field.

Linear programming analysis of loopy belief propagation for weighted matching

no code implementations NeurIPS 2007 Sujay Sanghavi, Dmitry Malioutov, Alan S. Willsky

Loopy belief propagation has been employed in a wide variety of applications with great empirical success, but it comes with few theoretical guarantees.

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