Content-Type: text/html R-LAIR: Riverside Lab for Artificial Intelligence Research

UCR

Computer Science and Engineering



R-LAIR: Riverside Lab for Artificial Intelligence Research

Auxiliary Gibbs Sampling for Inference in Piecewise-Constant Conditional Intensity Models (2015)

by Zhen Qin and Christian R. Shelton

Abstract: A piecewise-constant conditional intensity model (PCIM) is a non-Markovian model of temporal stochastic dependencies in continuous-time event streams. It allows efficient learning and forecasting given complete trajectories. However, no general inference algorithm has been developed for PCIMs. We propose an effective and efficient auxiliary Gibbs sampler for inference in PCIM, based on the idea of thinning for inhomogeneous Poisson processes. The sampler alternates between sampling a finite set of auxiliary virtual events with adaptive rates, and performing an efficient forward-backward pass at discrete times to generate samples. We show that our sampler can successfully perform inference tasks in both Markovian and non-Markovian models, and can be employed in Expectation-Maximization PCIM parameter estimation and structural learning with partially observed data.

Download Information

Zhen Qin and Christian R. Shelton (2015). "Auxiliary Gibbs Sampling for Inference in Piecewise-Constant Conditional Intensity Models." Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. pdf   code  

Bibtex citation

@inproceedings{QinShe15,
     author = "Zhen Qin and Christian R. Shelton",
     title = "Auxiliary Gibbs Sampling for Inference in Piecewise-Constant Conditional Intensity Models",
     booktitle = "Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence",
     booktitleabbr = "UAI",
     year = 2015,
}

full list

More Information

Address

University of California, Riverside
Chung Hall, room 368
Riverside, CA 92521

 


External Links