arXiv research

A new method scores contextual Markov networks without assuming chordality.

This paper reviews recent advances in Bayesian nonparametric techniques for constructing and performing inference in infinite hidden Markov models. We focus on variants of Bayesian nonparametric hidden Markov models that enhance a posteriori state-persistence in particular. This paper also introduces a new Bayesian non…

New algorithm learns causal structures by intersecting Markov blankets.

Paper uses genome Markov structure for outlier detection and read classification.

Framework integrates Markov and causal models for accurate counterfactual inference.

Generalizes bits back coding for time-series models with latent Markov structures.

A new heuristic for learning Markov network structure efficiently.

New tree-structured Markov fields with Poisson marginals for counting variables.

Modeling financial institution dependence structures for systemic risk.

New insights into Markov chain geometry via positive transition measures.

Generalized Precision Matrix for scalable estimation of nonparametric Markov networks.

A new sampler improves the inference of causal structures from observational data.

We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we \emph{directly} learn the network structure change by estimating the ratio of Markov network m…

Researchers calculate Shannon entropy rates of hidden Markov processes efficiently.

Study a risk model with tree-structured Poisson-Markov random field for rainfall events.

Max-min margin Markov networks improve consistency in structured prediction.

Neural Markov models improve time series analysis by balancing deep learning and classical models.

We investigate probabilistic graphical models that allow for both cycles and latent variables. For this we introduce directed graphs with hyperedges (HEDGes), generalizing and combining both marginalized directed acyclic graphs (mDAGs) that can model latent (dependent) variables, and directed mixed graphs (DMGs) that c…

The paper constructs Markov partitions for geodesic flow on hyperbolic surfaces.

In this paper, we present a novel and general framework called {\it Maximum Entropy Discrimination Markov Networks} (MaxEnDNet), which integrates the max-margin structured learning and Bayesian-style estimation and combines and extends their merits. Major innovations of this model include: 1) It generalizes the extant …

A new metric based on hitting probabilities for directed graphs and Markov chains.

The paper defines conditions for learning causal graphs from data with unobserved variables.

Kernel density estimators enhance Markov models with hidden states for complex data.

Paper relaxes faithfulness assumption for MB discovery in k-order Markov blanket.

Learning Markov blanket (MB) structures has proven useful in performing feature selection, learning Bayesian networks (BNs), and discovering causal relationships. We present a formula for efficiently determining the number of MB structures given a target variable and a set of other variables. As expected, the number of…

A new estimator for state values in reinforcement learning reduces complexity and improves convergence.

New method identifies nonstationary causal structures in time series data.

Recent years have seen an increasing popularity of learning the sparse \emph{changes} in Markov Networks. Changes in the structure of Markov Networks reflect alternations of interactions between random variables under different regimes and provide insights into the underlying system. While each individual network struc…

NMLNs use neural networks to learn relational structure from data.

Model interest rates and energy futures with regime-switching dynamics.

Paper proposes a new method for MRF structure learning.

There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension red…

In this paper, we propose a simple, versatile model for learning the structure and parameters of multivariate distributions from a data set. Learning a Markov network from a given data set is not a simple problem, because Markov networks rigorously represent Markov properties, and this rigor imposes complex constraints…

Differentiable structure learning addresses DAGs with multiple global minimizers.

Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a domain, more accurate joint probability distributions can be obtained for inference tasks or, more directly, for interpreting the most signifi…

Local method identifies causal relations in Markov equivalent DAGs.

Polynomial-time methods count and sample DAGs from Markov classes.

Algorithm finds ε-equilibrium policies for multi-agent Markov games with hidden low-rank structure.

A new method reduces CI tests for causal structure learning.

Pairwise methods outperform pseudo-likelihood in high-dimensional Markov network structure learning.

Privacy constraints affect learning Markov Random Fields differently.

Model reduction of Markov processes is a basic problem in modeling state-transition systems. Motivated by the state aggregation approach rooted in control theory, we study the statistical state compression of a discrete-state Markov chain from empirical trajectories. Through the lens of spectral decomposition, we study…

Study minimax optimal RL in factored MDPs with bonus exploration.

We present two algorithms for learning the structure of a Markov network from data: GSMN* and GSIMN. Both algorithms use statistical independence tests to infer the structure by successively constraining the set of structures consistent with the results of these tests. Until very recently, algorithms for structure lear…

Paper calculates KL divergence for isotropic Gaussian-Markov fields.

Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference …

Bayesian tensor factorization approximates a complex tree model.

In this paper, we present a simple non-parametric method for learning the structure of undirected graphs from data that drawn from an underlying unknown distribution. We propose to use Brownian distance covariance to estimate the conditional independences between the random variables and encodes pairwise Markov graph. …