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Tag: markov

  • Colored Markov Random Fields for Probabilistic Topological Modeling

    Colored Markov Random Fields for Probabilistic Topological Modeling arXiv:2512.03727v1 Announce Type: new Abstract: Probabilistic Graphical Models (PGMs) encode conditional dependencies among random variables using a graph -nodes for variables, links for dependencies- and factorize the joint distribution into lower-dimensional components. This makes PGMs well-suited for analyzing complex systems and supporting decision-making. Recent advances in topological…

  • A new class of Markov random fields enabling lightweight sampling

    A new class of Markov random fields enabling lightweight sampling arXiv:2511.02373v1 Announce Type: new Abstract: This work addresses the problem of efficient sampling of Markov random fields (MRF). The sampling of Potts or Ising MRF is most often based on Gibbs sampling, and is thus computationally expensive. We consider in this work how to circumvent…

  • Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space

    Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space arXiv:2510.12916v1 Announce Type: new Abstract: Systems of interacting continuous-time Markov chains are a powerful model class, but inference is typically intractable in high dimensional settings. Auxiliary information, such as noisy observations, is typically only available at discrete times, and incorporating it via…

  • Lower Bounds on the Size of Markov Equivalence Classes

    Lower Bounds on the Size of Markov Equivalence Classes arXiv:2506.20933v1 Announce Type: new Abstract: Causal discovery algorithms typically recover causal graphs only up to their Markov equivalence classes unless additional parametric assumptions are made. The sizes of these equivalence classes reflect the limits of what can be learned about the underlying causal graph from purely…

  • Generalization Bounds for Equivariant Networks on Markov Data

    Generalization Bounds for Equivariant Networks on Markov Data arXiv:2503.00292v1 Announce Type: new Abstract: Equivariant neural networks play a pivotal role in analyzing datasets with symmetry properties, particularly in complex data structures. However, integrating equivariance with Markov properties presents notable challenges due to the inherent dependencies within such data. Previous research has primarily concentrated on establishing…

  • Discrete Markov Probabilistic Models

    Discrete Markov Probabilistic Models arXiv:2502.07939v1 Announce Type: new Abstract: This paper introduces the Discrete Markov Probabilistic Model (DMPM), a novel algorithm for discrete data generation. The algorithm operates in the space of bits ${0,1}^d$, where the noising process is a continuous-time Markov chain that can be sampled exactly via a Poissonian clock that flips labels…