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Found 9 papers, 1 papers with code
Learning Gaussian Graphical Models with Observed or Latent FVSs
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.
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions
We consider the problem of Ising and Gaussian graphical model selection given n i. i. d.
Bayesian Nonparametric Inference of Switching Linear Dynamical Systems
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
We propose a Bayesian nonparametric approach to relating multiple time series via a set of latent, dynamical behaviors.
A sticky HDP-HMM with application to speaker diarization
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.
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.
Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis
We consider the estimation problem in Gaussian graphical models with arbitrary structure.
Loop Series and Bethe Variational Bounds in Attractive Graphical Models
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
Loopy belief propagation has been employed in a wide variety of applications with great empirical success, but it comes with few theoretical guarantees.
