
A Linear Approximation Method for Probabilistic Inference
An approximation method is presented for probabilistic inference with co...
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Local Probabilistic Model for Bayesian Classification: a Generalized Local Classification Model
In Bayesian classification, it is important to establish a probabilistic...
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Computation of Variances in Causal Networks
The causal (belief) network is a wellknown graphical structure for repr...
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CoarsetoFine Sequential Monte Carlo for Probabilistic Programs
Many practical techniques for probabilistic inference require a sequence...
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Exploiting Evidence in Probabilistic Inference
We define the notion of compiling a Bayesian network with evidence and p...
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Declarative Modeling and Bayesian Inference of Dark Matter Halos
Probabilistic programming allows specification of probabilistic models i...
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When do Numbers Really Matter?
Common wisdom has it that small distinctions in the probabilities quanti...
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ContextSpecific Approximation in Probabilistic Inference
There is evidence that the numbers in probabilistic inference don't really matter. This paper considers the idea that we can make a probabilistic model simpler by making fewer distinctions. Unfortunately, the level of a Bayesian network seems too coarse; it is unlikely that a parent will make little difference for all values of the other parents. In this paper we consider an approximation scheme where distinctions can be ignored in some contexts, but not in other contexts. We elaborate on a notion of a parent context that allows a structured contextspecific decomposition of a probability distribution and the associated probabilistic inference scheme called probabilistic partial evaluation (Poole 1997). This paper shows a way to simplify a probabilistic model by ignoring distinctions which have similar probabilities, a method to exploit the simpler model, a bound on the resulting errors, and some preliminary empirical results on simple networks.
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