
Probabilistic Programs with Stochastic Conditioning
We tackle the problem of conditioning probabilistic programs on distribu...
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Relations among conditional probabilities
We describe a Groebner basis of relations among conditional probabilitie...
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Using Eigencentrality to Estimate Joint, Conditional and Marginal Probabilities from MixedVariable Data: Method and Applications
The ability to estimate joint, conditional and marginal probability dist...
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Calculating Uncertainty Intervals From Conditional Convex Sets of Probabilities
In Moral, Campos (1991) and Cano, Moral, VerdegayLopez (1991) a new met...
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A Probabilistic Calculus of Actions
We present a symbolic machinery that admits both probabilistic and causa...
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Tractable Inference in Credal Sentential Decision Diagrams
Probabilistic sentential decision diagrams are logic circuits where the ...
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MESA: Maximum Entropy by Simulated Annealing
Probabilistic reasoning systems combine different probabilistic rules an...
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Sufficiency, Separability and Temporal Probabilistic Models
Suppose we are given the conditional probability of one variable given some other variables.Normally the full joint distribution over the conditioning variablesis required to determine the probability of the conditioned variable.Under what circumstances are the marginal distributions over the conditioning variables sufficient to determine the probability ofthe conditioned variable?Sufficiency in this sense is equivalent to additive separability ofthe conditional probability distribution.Such separability structure is natural and can be exploited forefficient inference.Separability has a natural generalization to conditional separability.Separability provides a precise notion of weaklyinteracting subsystems in temporal probabilistic models.Given a system that is decomposed into separable subsystems, exactmarginal probabilities over subsystems at future points in time can becomputed by propagating marginal subsystem probabilities, rather thancomplete system joint probabilities.Thus, separability can make exact prediction tractable.However, observations can break separability,so exact monitoring of dynamic systems remains hard.
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