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Found 12 papers, 2 papers with code
A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations
A Bayesian network can be learned from data using the well-known score-and-search approach, and within this approach a key consideration is how to simultaneously learn the global structure in the form of the underlying DAG and the local structure in the CPDs.
Learning All Credible Bayesian Network Structures for Model Averaging
However, selecting a single model (i. e., the best scoring BN) can be misleading or may not achieve the best possible accuracy.
Kernel-based Approach to Handle Mixed Data for Inferring Causal Graphs
The advantage of this idea is that is possible to handle any data type by using a suitable kernel function to compute a kernel matrix for an observed variable.
On Pruning for Score-Based Bayesian Network Structure Learning
Many algorithms for score-based Bayesian network structure learning (BNSL), in particular exact ones, take as input a collection of potentially optimal parent sets for each variable in the data.
Online Causal Structure Learning in the Presence of Latent Variables
We present two online causal structure learning algorithms which can track changes in a causal structure and process data in a dynamic real-time manner.
Finding All Bayesian Network Structures within a Factor of Optimal
However, selecting a single model (i. e., the best scoring BN) can be misleading or may not achieve the best possible accuracy.
Finding Minimal Cost Herbrand Models with Branch-Cut-and-Price
Given (1) a set of clauses $T$ in some first-order language $\cal L$ and (2) a cost function $c : B_{{\cal L}} \rightarrow \mathbb{R}_{+}$, mapping each ground atom in the Herbrand base $B_{{\cal L}}$ to a non-negative real, then the problem of finding a minimal cost Herbrand model is to either find a Herbrand model $\cal I$ of $T$ which is guaranteed to minimise the sum of the costs of true ground atoms, or establish that there is no Herbrand model for $T$.
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets, and Complexity
The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research.
First-order integer programming for MAP problems
Finding the most probable (MAP) model in SRL frameworks such as Markov logic and Problog can, in principle, be solved by encoding the problem as a `grounded-out' mixed integer program (MIP).
Exact Estimation of Multiple Directed Acyclic Graphs
This paper considers the problem of estimating the structure of multiple related directed acyclic graph (DAG) models.
Advances in Bayesian Network Learning using Integer Programming
We consider the problem of learning Bayesian networks (BNs) from complete discrete data.
Bayesian network learning with cutting planes
The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered.
