3 research outputs found
Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks
We present an algorithm for arc reversal in Bayesian networks with
tree-structured conditional probability tables, and consider some of its
advantages, especially for the simulation of dynamic probabilistic networks. In
particular, the method allows one to produce CPTs for nodes involved in the
reversal that exploit regularities in the conditional distributions. We argue
that this approach alleviates some of the overhead associated with arc
reversal, plays an important role in evidence integration and can be used to
restrict sampling of variables in DPNs. We also provide an algorithm that
detects the dynamic irrelevance of state variables in forward simulation. This
algorithm exploits the structured CPTs in a reversed network to determine, in a
time-independent fashion, the conditions under which a variable does or does
not need to be sampled.Comment: Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997
A Critique on the Interventional Detection of Causal Relationships
Interventions are of fundamental importance in Pearl's probabilistic
causality regime. In this paper, we will inspect how interventions influence
the interpretation of causation in causal models in specific situation. To this
end, we will introduce a priori relationships as non-causal relationships in a
causal system. Then, we will proceed to discuss the cases that interventions
can lead to spurious causation interpretations. This includes the
interventional detection of a priori relationships, and cases where the
interventional detection of causality forms structural causal models that are
not valid in natural situations. We will also discuss other properties of a
priori relations and SCMs that have a priori information in their structural
equations.Comment: 15 pages, 6 figure
Structured arc reversal and simulation of dynamic probabilistic networks
We present an algorithm for arc reversal in Bayesian networks with tree-structured conditional probability tables, and consider some of its advantages, especially for the simulation of dynamic probabilistic networks. In particular, the method allows one to produce CPTs for nodes involved in the reversal that exploit regularities in the conditional distributions. We argue that this approach alleviates some of the overhead associated with arc reversal, plays an important role in evidence integration and can be used to restrict sampling of variables in DPNs. We also provide an algorithm that detects the dynamic irrelevance of state variables in forward simulation. This algorithm exploits the structured CPTs in a reversed network to determine, in a timeindependent fashion, the conditions under which a variable does or does not need to be sampled.