ExDBN: Exact learning of Dynamic Bayesian Networks

• URL: http://arxiv.org/abs/2410.16100v2
• Date: Tue, 22 Oct 2024 12:16:03 GMT
• Title: ExDBN: Exact learning of Dynamic Bayesian Networks
• Authors: Pavel Rytir, Ales Wodecki, Georgios Korpas, Jakub Marecek,
• Abstract summary: We propose a score-based learning approach for causal learning from data. We show that the proposed approach turns out to produce excellent results when applied to small and medium-sized synthetic instances of up to 25 time-series. Two interesting applications in bio-science and finance, to which the method is directly applied, further stress the opportunities in developing highly accurate, globally convergent solvers.
• Score: 2.2499166814992435
• License:
• Abstract: Causal learning from data has received much attention in recent years. One way of capturing causal relationships is by utilizing Bayesian networks. There, one recovers a weighted directed acyclic graph, in which random variables are represented by vertices, and the weights associated with each edge represent the strengths of the causal relationships between them. This concept is extended to capture dynamic effects by introducing a dependency on past data, which may be captured by the structural equation model, which is utilized in the present contribution to formulate a score-based learning approach. A mixed-integer quadratic program is formulated and an algorithmic solution proposed, in which the pre-generation of exponentially many acyclicity constraints is avoided by utilizing the so-called branch-and-cut ("lazy constraint") method. Comparing the novel approach to the state of the art, we show that the proposed approach turns out to produce excellent results when applied to small and medium-sized synthetic instances of up to 25 time-series. Lastly, two interesting applications in bio-science and finance, to which the method is directly applied, further stress the opportunities in developing highly accurate, globally convergent solvers that can handle modest instances.

Related papers

• Differentiable Bayesian Structure Learning with Acyclicity Assurance [7.568978862189266]
  We propose an alternative approach for strictly constraining the acyclicty of the graphs with an integration of the knowledge from the topological orderings. Our approach can reduce inference complexity while ensuring the structures of the generated graphs to be acyclic.
  arXiv  Detail & Related papers  (2023-09-04T06:44:46Z)
• ChiroDiff: Modelling chirographic data with Diffusion Models [132.5223191478268]
  We introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate.
  arXiv  Detail & Related papers  (2023-04-07T15:17:48Z)
• Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations [57.15855198512551]
  We propose a novel score-based generative model for graphs with a continuous-time framework. We show that our method is able to generate molecules that lie close to the training distribution yet do not violate the chemical valency rule.
  arXiv  Detail & Related papers  (2022-02-05T08:21:04Z)
• Learning Gaussian Graphical Models with Latent Confounders [74.72998362041088]
  We compare and contrast two strategies for inference in graphical models with latent confounders. While these two approaches have similar goals, they are motivated by different assumptions about confounding. We propose a new method, which combines the strengths of these two approaches.
  arXiv  Detail & Related papers  (2021-05-14T00:53:03Z)
• One-shot Learning for Temporal Knowledge Graphs [49.41854171118697]
  We propose a one-shot learning framework for link prediction in temporal knowledge graphs. Our proposed method employs a self-attention mechanism to effectively encode temporal interactions between entities. Our experiments show that the proposed algorithm outperforms the state of the art baselines for two well-studied benchmarks.
  arXiv  Detail & Related papers  (2020-10-23T03:24:44Z)
• Differentiable Causal Discovery from Interventional Data [141.41931444927184]
  We propose a theoretically-grounded method based on neural networks that can leverage interventional data. We show that our approach compares favorably to the state of the art in a variety of settings.
  arXiv  Detail & Related papers  (2020-07-03T15:19:17Z)
• Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks [2.7473982588529653]
  We study the problem of learning the sparse DAG structure of a BN from continuous observational data. The optimal solution to this mathematical program is known to have desirable statistical properties under certain conditions. We propose a concrete early stopping criterion to terminate the branch-and-bound process in order to obtain a near-optimal solution.
  arXiv  Detail & Related papers  (2020-05-29T00:13:15Z)
• DYNOTEARS: Structure Learning from Time-Series Data [6.7638850283606855]
  We propose a method that simultaneously estimates contemporaneous (intra-slice) and time-lagged (inter-slice) relationships between variables in a time-series. Compared to state-of-the-art methods for learning dynamic Bayesian networks, our method is both scalable and accurate on real data.
  arXiv  Detail & Related papers  (2020-02-02T21:47:48Z)
• Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies [83.05591911173332]
  junction tree algorithm is the most general solution for exact MAP inference with run-time guarantees. We propose a new graph transformation technique via node cloning which ensures a run-time for solving our target problem independently of the form of a corresponding clique tree.
  arXiv  Detail & Related papers  (2019-12-27T13:30:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.