Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Gelfand, Andrew"'
Autor:
Oluwafemi, Omobola, Manoharan, Sneha, Xie, Luyu, Pro, George, Patel, Rikinkumar S., Delclos, George L., Gelfand, Andrew, Messiah, Sarah E., Lopez, David S., Patel, Jenil
Publikováno v:
In Pediatric Neurology July 2024 156:131-138
Autor:
Xie, Luyu, Chandrasekhar, Aparajita, Ernest, Deepali, Patel, Jenil, Afolabi, Folashade, Almandoz, Jaime P, Martinez Fernandez, Tanya, Gelfand, Andrew, Messiah, Sarah E.
Publikováno v:
Journal of Asthma; Apr2024, Vol. 61 Issue 4, p368-376, 9p
This manuscript discusses computation of the Partition Function (PF) and the Minimum Weight Perfect Matching (MWPM) on arbitrary, non-bipartite graphs. We present two novel problem formulations - one for computing the PF of a Perfect Matching (PM) an
Externí odkaz:
http://arxiv.org/abs/1306.1267
We study the Maximum Weight Matching (MWM) problem for general graphs through the max-product Belief Propagation (BP) and related Linear Programming (LP). The BP approach provides distributed heuristics for finding the Maximum A Posteriori (MAP) assi
Externí odkaz:
http://arxiv.org/abs/1306.1167
Belief Propagation (BP) is a popular, distributed heuristic for performing MAP computations in Graphical Models. BP can be interpreted, from a variational perspective, as minimizing the Bethe Free Energy (BFE). BP can also be used to solve a special
Externí odkaz:
http://arxiv.org/abs/1305.4130
We introduce a new cluster-cumulant expansion (CCE) based on the fixed points of iterative belief propagation (IBP). This expansion is similar in spirit to the loop-series (LS) recently introduced in [1]. However, in contrast to the latter, the CCE e
Externí odkaz:
http://arxiv.org/abs/1210.4916
Autor:
Gelfand, Andrew E., Welling, Max
This paper provides some new guidance in the construction of region graphs for Generalized Belief Propagation (GBP). We connect the problem of choosing the outer regions of a LoopStructured Region Graph (SRG) to that of finding a fundamental cycle ba
Externí odkaz:
http://arxiv.org/abs/1210.4857
A major limitation of exact inference algorithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend inference algorithms, particularly B
Externí odkaz:
http://arxiv.org/abs/1203.3487
Autor:
Xie, Luyu, Gelfand, Andrew, Mathew, M. Sunil, Atem, Folefac D., Delclos, George L., Messiah, Sarah E.
Publikováno v:
Journal of Asthma; Apr2023, Vol. 60 Issue 4, p698-707, 10p
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