Paradox
发表于 2025-3-30 09:33:03
An Operational View of Coherent Conditional Previsionscedure turns out to be helpful when the size of the relevant quantities strongly depend on the different scenarios in which they are considered. A simple example shows the potentiality of the entire machinery on a decision-aid problem.
Morbid
发表于 2025-3-30 13:26:08
Decomposition of Influence Diagramstly. The method is based on an operational characterization of the future variables being relevant for a decision variable, thereby also providing a characterization of those parts of a decision problem that are relevant for a particular decision.
净礼
发表于 2025-3-30 20:29:22
Using Recursive Decomposition to Construct Elimination Orders, Jointrees, and Dtreesing. This new method turns out to be quite competitive with existing methods in minimizing width. We also present methods for converting a dtree of width . into elimination orders and jointrees of no greater width. This leads to a new class of algorithms for generating elimination orders and jointrees (via recursive decomposition).
弯弯曲曲
发表于 2025-3-31 00:12:32
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吞没
发表于 2025-3-31 02:19:50
Space-Progressive Value Iteration: An Anytime Algorithm for a Class of POMDPs the entire belief space. Rather it restricts DP updates to a belief subspace that grows over time. It is argued that given sufficient time SPVI can find near-optimal policies for almost discernible POMDPs. We then show how SPVI can be applied to more a general class of POMDPs. Empirical results are presented to show the effectiveness of SPVI.
entail
发表于 2025-3-31 08:40:56
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多节
发表于 2025-3-31 10:32:45
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WATER
发表于 2025-3-31 16:27:53
Supporting Changes in Structure in Causal Model Constructiong an endogenous variable exogenous), which mechanisms are possibly invalidated and can be removed from the model? (2) Which variables may be manipulated in order to invalidate and, effectively, remove a mechanism from a model?
conjunctivitis
发表于 2025-3-31 20:04:26
Conference proceedings 2001decision-making, coherent probabilities, Bayesian networks, learning causal networks, graphical representation of uncertainty, imprecise probabilities, belief functions, fuzzy sets and rough sets, possibility theory, merging, belief revision and preferences, inconsistency handling, default logic, logic programming, etc.
dagger
发表于 2025-3-31 23:28:15
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