Learning optimal bounded treewidth Bayesian networks via maximum satisfiability
J Berg, M Järvisalo, B Malone - Artificial Intelligence and …, 2014 - proceedings.mlr.press
Bayesian network structure learning is the well-known computationally hard problem of
finding a directed acyclic graph structure that optimally describes given data. A learned …
finding a directed acyclic graph structure that optimally describes given data. A learned …
Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes
Greedy Equivalence Search (GES) is nowadays the state of the art algorithm for learning
Bayesian networks (BNs) from complete data. However, from a practical point of view, this …
Bayesian networks (BNs) from complete data. However, from a practical point of view, this …
Polyhedral aspects of score equivalence in Bayesian network structure learning
This paper deals with faces and facets of the family-variable polytope and the characteristic-
imset polytope, which are special polytopes used in integer linear programming approaches …
imset polytope, which are special polytopes used in integer linear programming approaches …
Characteristic imsets for learning Bayesian network structure
R Hemmecke, S Lindner, M Studený - International Journal of Approximate …, 2012 - Elsevier
The motivation for the paper is the geometric approach to learning Bayesian network (BN)
structure. The basic idea of our approach is to represent every BN structure by a certain …
structure. The basic idea of our approach is to represent every BN structure by a certain …
Exact estimation of multiple directed acyclic graphs
This paper considers structure learning for multiple related directed acyclic graph (DAG)
models. Building on recent developments in exact estimation of DAGs using integer linear …
models. Building on recent developments in exact estimation of DAGs using integer linear …
Evaluating structure learning algorithms with a balanced scoring function
AC Constantinou - arXiv preprint arXiv:1905.12666, 2019 - arxiv.org
Several structure learning algorithms have been proposed towards discovering causal or
Bayesian Network (BN) graphs. The validity of these algorithms tends to be evaluated by …
Bayesian Network (BN) graphs. The validity of these algorithms tends to be evaluated by …
Generalized permutohedra from probabilistic graphical models
A graphical model encodes conditional independence relations via the Markov properties.
For an undirected graph these conditional independence relations can be represented by a …
For an undirected graph these conditional independence relations can be represented by a …
Unifying causal inference and reinforcement learning using higher-order category theory
S Mahadevan - arXiv preprint arXiv:2209.06262, 2022 - arxiv.org
We present a unified formalism for structure discovery of causal models and predictive state
representation (PSR) models in reinforcement learning (RL) using higher-order category …
representation (PSR) models in reinforcement learning (RL) using higher-order category …
[PDF][PDF] Characteristic imset: a simple algebraic representative of a Bayesian network structure
M Studený, R Hemmecke, S Lindner - … of the 5th European workshop on …, 2010 - Citeseer
First, we recall the basic idea of an algebraic and geometric approach to learning a
Bayesian network (BN) structure proposed in (Studený, Vomlel and Hemmecke, 2010): to …
Bayesian network (BN) structure proposed in (Studený, Vomlel and Hemmecke, 2010): to …