Papers
Bayesian Network Structure Learning with Side Constraints
Andrew Li, Peter Beek
Learning Non-parametric Markov Networks with Mutual Information
Janne Leppä-Aho, Santeri Räisänen, Xiao Yang et al.
A sum-product algorithm with polynomials for computing exact derivatives of the likelihood in Bayesian networks
Alexandra Lefebvre, Grégory Nuel
Finding Minimal Separators in LWF Chain Graphs
Mohammad Ali Javidian, Marco Valtorta
Prometheus : Directly Learning Acyclic Directed Graph Structures for Sum-Product Networks
Priyank Jaini, Amur Ghose, Pascal Poupart
Instance-Specific Bayesian Network Structure Learning
Fattaneh Jabbari, Shyam Visweswaran, Gregory F. Cooper
On the Relative Expressiveness of Bayesian and Neural Networks
Arthur Choi, Adnan Darwiche
Solving M-Modes in Loopy Graphs Using Tree Decompositions
Cong Chen, Changhe Yuan, Ze Ye et al.
Structure Learning for Bayesian Networks over Labeled DAGs
Antti Hyttinen, Johan Pensar, Juha Kontinen et al.
Structure Learning Under Missing Data
Alexander Gain, Ilya Shpitser
Parameterized hardness of active inference
Nils Donselaar
Parallel Probabilistic Inference by Weighted Model Counting
Giso H. Dal, Alfons W. Laarman, Peter J.F. Lucas
Markov Random Field MAP as Set Partitioning
James Cussens
Cascading Sum-Product Networks using Robustness
Diarmaid Conaty, Jesús Martínez Del Rincon, Cassio P. De Campos
A partial orthogonalization method for simulating covariance and concentration graph matrices
Irene Córdoba, Gherardo Varando, Concha Bielza et al.
An Empirical Study of Methods for SPN Learning and Inference
Cory J. Butz, Jhonatan S. Oliveira, André E. Santos et al.
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
Ioan Gabriel Bucur, Tom Bussel, Tom Claassen et al.
An Order-based Algorithm for Learning Structure of Bayesian Networks
Shahab Behjati, Hamid Beigy
Causal Structure Learning via Temporal Markov Networks
Aubrey Barnard, David Page
Bayesian Network Classifiers Under the Ensemble Perspective
Jacinto Arias, José A. Gámez, José M. Puerta
Proceedings of the 9th International Conference on Probabilistic Graphical Models
Václav Kratochvíl, Milan Studený
Flare: Optimizing Apache Spark with Native Compilation for Scale-Up Architectures and Medium-Size Data
Gregory Essertel, Ruby Tahboub, James Decker et al.
Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows
Vasiliki Kalavri, John Liagouris, Moritz Hoffmann et al.
RStream: Marrying Relational Algebra with Streaming for Efficient Graph Mining on A Single Machine
Kai Wang, Zhiqiang Zuo, John Thorpe et al.
ASAP: Fast, Approximate Graph Pattern Mining at Scale
Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin et al.