Stochastic Simulation Algorithms for Dynamic Probabilistic Networks
Keiji Kanazawa, Daphne Koller, Stuart Russell

TL;DR
This paper introduces two novel stochastic simulation algorithms, ER and SOF, designed for dynamic probabilistic networks, improving accuracy over time and maintaining bounded error in temporal process modeling.
Contribution
The paper proposes ER and SOF algorithms that adapt simulation trials using evidence, addressing divergence issues in dynamic probabilistic networks.
Findings
ER and SOF outperform likelihood weighting in DPNs
Combining ER and SOF maintains bounded error over time
Algorithms improve simulation accuracy in stochastic temporal models
Abstract
Stochastic simulation algorithms such as likelihood weighting often give fast, accurate approximations to posterior probabilities in probabilistic networks, and are the methods of choice for very large networks. Unfortunately, the special characteristics of dynamic probabilistic networks (DPNs), which are used to represent stochastic temporal processes, mean that standard simulation algorithms perform very poorly. In essence, the simulation trials diverge further and further from reality as the process is observed over time. In this paper, we present simulation algorithms that use the evidence observed at each time step to push the set of trials back towards reality. The first algorithm, "evidence reversal" (ER) restructures each time slice of the DPN so that the evidence nodes for the slice become ancestors of the state variables. The second algorithm, called "survival of the fittest"…
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Taxonomy
TopicsBayesian Modeling and Causal Inference · AI-based Problem Solving and Planning · Software Reliability and Analysis Research
