Linear chains and trees are basic building blocks in many applications of graphi-cal models, and they admit simple exact maximum a-posteriori (MAP) inference algorithms based on message passing. However, in many cases this computa-tion is prohibitively expensive, due to quadratic dependence on variables ’ domain sizes. The standard algorithms are inefficient because they compute scores for hypotheses for which there is strong negative local evidence. For this reason there has been significant previous interest in beam search and its variants; how-ever, these methods provide only approximate results. This paper presents new exact inference algorithms based on the combination of column generation and pre-computed bounds on terms of the model’...
The problem of finding the most probable explanation to a designated set of vari-ables given partial...
We show that the expected computational complexity of the Junction-Tree Algorithm for maximum a post...
This paper describes our entry for the MAP/MPE track of the PASCAL 2011 Probabilistic Inference Chal...
Linear chains and trees are basic building blocks in many applications of graphical models. Although...
In this paper, we show how the connections between max-product message passing for max-product and l...
The MAP (maximum a posteriori hypothesis) problem in Bayesian networks is to find the most likely st...
Maximum A Posteriori inference in graphical models is often solved via message-passing algorithms, s...
Maximum a-posteriori (MAP) query in statistical relational models computes the most probable world g...
Abstract. This article presents a new search algorithm for the NP-hard problem of optimizing functio...
Graphical models have become a central paradigm for knowledge representation and rea- soning over mo...
<p>Exact MAP computation can be performed by enumerating all possible genotype configurations. Becau...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
ROCKIT is a maximum a-posteriori (MAP) query en-gine for statistical relational models. MAP inferenc...
International audienceProbabilistic graphical models offer a powerful framework to account for the d...
A fast response is critical in many data-intensive applications, including knowledge discovery analy...
The problem of finding the most probable explanation to a designated set of vari-ables given partial...
We show that the expected computational complexity of the Junction-Tree Algorithm for maximum a post...
This paper describes our entry for the MAP/MPE track of the PASCAL 2011 Probabilistic Inference Chal...
Linear chains and trees are basic building blocks in many applications of graphical models. Although...
In this paper, we show how the connections between max-product message passing for max-product and l...
The MAP (maximum a posteriori hypothesis) problem in Bayesian networks is to find the most likely st...
Maximum A Posteriori inference in graphical models is often solved via message-passing algorithms, s...
Maximum a-posteriori (MAP) query in statistical relational models computes the most probable world g...
Abstract. This article presents a new search algorithm for the NP-hard problem of optimizing functio...
Graphical models have become a central paradigm for knowledge representation and rea- soning over mo...
<p>Exact MAP computation can be performed by enumerating all possible genotype configurations. Becau...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
ROCKIT is a maximum a-posteriori (MAP) query en-gine for statistical relational models. MAP inferenc...
International audienceProbabilistic graphical models offer a powerful framework to account for the d...
A fast response is critical in many data-intensive applications, including knowledge discovery analy...
The problem of finding the most probable explanation to a designated set of vari-ables given partial...
We show that the expected computational complexity of the Junction-Tree Algorithm for maximum a post...
This paper describes our entry for the MAP/MPE track of the PASCAL 2011 Probabilistic Inference Chal...