Add to ORKGProbabilistic generating circuits (PGCs) are economical representations of multivariate probability generating polynomials (PGPs). They unify and extend decomposable probabilistic circuits and determinantal point processes, admitting tractable computation of marginal probabilities. However, the need for addition and multiplication of high-degree polynomials incurs a significant additional factor in the complexity of inference. Here, we give a new inference algorithm that eliminates this extra factor. Specifically, we show that it suffices to keep track of the highest degree coefficients of the computed polynomials, rendering the algorithm linear in the circuit size. In addition, we show that determinant-based circuits need not be expanded t...
The author shows a uniform circuit characterization of BP.⊕𝒫 without using probabilistic bits. ...
Standard approaches to probabilistic reasoning require that one possesses an explicit model of the d...
AbstractWe show that, for every Boolean function f(x1, …, xn) in the class AC0 and an arbitrary cons...
Probabilistic graphical models have been successfully applied to a wide variety of fields such as co...
We present various applications of the probabilistic method and polynomial method in additive combin...
Probabilistic logical languages provide powerful formalisms for knowledge representation and learnin...
On one side, symbolic methods represent our knowledge of the world, and when coupled with probabilis...
The biggest limitation of probabilistic graphical models is the complexity of inference, which is of...
Density estimation could be viewed as a core component in machine learning, since a good estimator c...
Causal inference provides a means of translating a target causal query into a causal formula, which ...
In the first article we present a network based algorithm for probabilistic inference in an undirect...
The probabilistic sentential decision diagram (PSDD) was recently introduced as a tractable represen...
Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wid...
11 pagesInternational audienceArithmetic circuits (AC) are circuits over the real numbers with 0/1-v...
Tractable learning aims to learn probabilistic models where inference is guaran-teed to be efficient...
The author shows a uniform circuit characterization of BP.⊕𝒫 without using probabilistic bits. ...
Standard approaches to probabilistic reasoning require that one possesses an explicit model of the d...
AbstractWe show that, for every Boolean function f(x1, …, xn) in the class AC0 and an arbitrary cons...
Probabilistic graphical models have been successfully applied to a wide variety of fields such as co...
We present various applications of the probabilistic method and polynomial method in additive combin...
Probabilistic logical languages provide powerful formalisms for knowledge representation and learnin...
On one side, symbolic methods represent our knowledge of the world, and when coupled with probabilis...
The biggest limitation of probabilistic graphical models is the complexity of inference, which is of...
Density estimation could be viewed as a core component in machine learning, since a good estimator c...
Causal inference provides a means of translating a target causal query into a causal formula, which ...
In the first article we present a network based algorithm for probabilistic inference in an undirect...
The probabilistic sentential decision diagram (PSDD) was recently introduced as a tractable represen...
Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wid...
11 pagesInternational audienceArithmetic circuits (AC) are circuits over the real numbers with 0/1-v...
Tractable learning aims to learn probabilistic models where inference is guaran-teed to be efficient...
The author shows a uniform circuit characterization of BP.⊕𝒫 without using probabilistic bits. ...
Standard approaches to probabilistic reasoning require that one possesses an explicit model of the d...
AbstractWe show that, for every Boolean function f(x1, …, xn) in the class AC0 and an arbitrary cons...