Reasoning at the Right Time Granularity
Suchi Saria, Uri Nodelman, Daphne Koller

TL;DR
This paper introduces a novel inference algorithm for Continuous Time Bayesian Networks that dynamically adjusts the temporal granularity of different system components, leading to more efficient and adaptive reasoning in complex dynamic systems.
Contribution
It proposes a new EP algorithm with a flexible cluster graph architecture that adapts time granularity during inference based on system evolution rates.
Findings
Significant computational savings demonstrated in experiments
Adaptive granularity improves inference efficiency
Dynamic re-partitioning enhances model flexibility
Abstract
Most real-world dynamic systems are composed of different components that often evolve at very different rates. In traditional temporal graphical models, such as dynamic Bayesian networks, time is modeled at a fixed granularity, generally selected based on the rate at which the fastest component evolves. Inference must then be performed at this fastest granularity, potentially at significant computational cost. Continuous Time Bayesian Networks (CTBNs) avoid time-slicing in the representation by modeling the system as evolving continuously over time. The expectation-propagation (EP) inference algorithm of Nodelman et al. (2005) can then vary the inference granularity over time, but the granularity is uniform across all parts of the system, and must be selected in advance. In this paper, we provide a new EP algorithm that utilizes a general cluster graph architecture where clusters…
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Gaussian Processes and Bayesian Inference · Time Series Analysis and Forecasting
