[PDF][PDF] Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming.
Recent approaches to causal discovery based on Boolean satisfiability solvers have opened
new opportunities to consider search spaces for causal models with both feedback cycles …
new opportunities to consider search spaces for causal models with both feedback cycles …
SAT modulo graphs: Acyclicity
Acyclicity is a recurring property of solutions to many important combinatorial problems. In
this work we study embeddings of specialized acyclicity constraints in the satisfiability …
this work we study embeddings of specialized acyclicity constraints in the satisfiability …
Marginal pseudo-likelihood learning of discrete Markov network structures
J Pensar, H Nyman, J Niiranen, J Corander - 2017 - projecteuclid.org
Marginal Pseudo-Likelihood Learning of Discrete Markov Network Structures Page 1
Bayesian Analysis (2017) 12, Number 4, pp. 1195–1215 Marginal Pseudo-Likelihood …
Bayesian Analysis (2017) 12, Number 4, pp. 1195–1215 Marginal Pseudo-Likelihood …
Answer set programming modulo acyclicity
Acyclicity constraints are prevalent in knowledge representation and applications where
acyclic data structures such as DAGs and trees play a role. Recently, such constraints have …
acyclic data structures such as DAGs and trees play a role. Recently, such constraints have …
Answer set programming as SAT modulo acyclicity
Answer set programming (ASP) is a declarative programming paradigm for solving search
problems arising in knowledge-intensive domains. One viable way to implement the …
problems arising in knowledge-intensive domains. One viable way to implement the …
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 …
Improving the normalization of weight rules in answer set programs
Cardinality and weight rules are important primitives in answer set programming. In this
context, normalization means the translation of such rules back into normal rules, eg, for the …
context, normalization means the translation of such rules back into normal rules, eg, for the …
Learning large Bayesian networks with expert constraints
We propose a new score-based algorithm for learning the structure of a Bayesian Network
(BN). It is the first algorithm that simultaneously supports the requirements of (i) learning a …
(BN). It is the first algorithm that simultaneously supports the requirements of (i) learning a …
Propositional encodings of acyclicity and reachability by using vertex elimination
We introduce novel methods for encoding acyclicity and st-reachability constraints for
propositional formulas with underlying directed graphs, based on vertex elimination graphs …
propositional formulas with underlying directed graphs, based on vertex elimination graphs …
[HTML][HTML] Towards using the chordal graph polytope in learning decomposable models
The motivation for this paper is the integer linear programming approach to learning the
structure of a decomposable graphical model. We have chosen to represent decomposable …
structure of a decomposable graphical model. We have chosen to represent decomposable …