A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New algorithm for collective Gaussian hidden Markov models inference.
Generalizes bits back coding for time-series models with latent Markov structures.
Markov models lie at the interface between statistical independence in a probability distribution and graph separation properties. We review model selection and estimation in directed and undirected Markov models with Gaussian parametrization, emphasizing the main similarities and differences. These two model classes a…
A new HMM model captures kernel dependencies using context-specific Bayesian networks.
Given a Gaussian Markov random field, we consider the problem of selecting a subset of variables to observe which minimizes the total expected squared prediction error of the unobserved variables. We first show that finding an exact solution is NP-hard even for a restricted class of Gaussian Markov random fields, calle…
Paper calculates KL divergence for isotropic Gaussian-Markov fields.
This is a technical report which explores the estimation methodologies on hyper-parameters in Markov Random Field and Gaussian Hidden Markov Random Field. In first section, we briefly investigate a theoretical framework on Metropolis-Hastings algorithm. Next, by using MH algorithm, we simulate the data from Ising model…
Novel CMG framework improves financial sentiment forecasting.
New algorithm estimates causal effects for non-Gaussian data.
Banded matrices can be used as precision matrices in several models including linear state-space models, some Gaussian processes, and Gaussian Markov random fields. The aim of the paper is to make modern inference methods (such as variational inference or gradient-based sampling) available for Gaussian models with band…
A Python package for GLHMM, a flexible HMM framework.
New algorithm learns HMM parameters on Riemannian manifolds.
A scalable deep GMRF model for general graphs improves predictions and uncertainty estimates.
This paper formed part of a preliminary research report for a risk consultancy and academic research. Stochastic Programming models provide a powerful paradigm for decision making under uncertainty. In these models the uncertainties are represented by a discrete scenario tree and the quality of the solutions obtained i…
Markov chain decoders improve generative models' ability to produce heavy-tailed data.
New methods reduce computational cost for Gaussian Markov Random Fields with sparse constraints.
New method speeds up sampling of Markov random fields.
CMRFs extend PGMs for topological data, capturing both conditional and marginal dependencies.
Extends Gaussian process regression for non-Gaussian data.
Algorithm learns non-Gaussian graphical models via Hessian scores and triangular transport.
Gaussian Belief Propagation (BP) algorithm is one of the most important distributed algorithms in signal processing and statistical learning involving Markov networks. It is well known that the algorithm correctly computes marginal density functions from a high dimensional joint density function over a Markov network i…
Deep GMRFs improve spatial data modeling and prediction.
Stein variational gradient descent improves inference in Gaussian process models.
TAGM models time-varying connections between variables.
Efficiently trains deep Gaussian processes with sparse approximations.
In this paper, we present a novel framework incorporating a combination of sparse models in different domains. We posit the observed data as generated from a linear combination of a sparse Gaussian Markov model (with a sparse precision matrix) and a sparse Gaussian independence model (with a sparse covariance matrix). …
A scalable Bayesian additive model for stellar flare detection using Gaussian process inference and hidden Markov models.
Bayesian method learns network structure from Gaussian process priors.
Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models. Identifying jump Markov linear models makes for a challenging problem lacking an analytical solution. We derive a new expectation maximization …
New sampling methods improve statistical efficiency for intractable targets.
Model detects epileptic seizures in EEG with high sensitivity.
New algorithms sample convex bodies using Markov chains and restricted Gaussian oracles.
Proposes a Riemannian optimization for policy improvement in MDPs.
A multi-task GP model tracks time-varying transition probabilities between two states.
Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class classification. A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued rando…
Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic c…
In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm. Our method can solve Bayesian image denoising problems, including hyperparameter estimation, in -time, where is the number of pixels in a given image. From the persp…
We present a new family of models that is based on graphs that may have undirected, directed and bidirected edges. We name these new models marginal AMP (MAMP) chain graphs because each of them is Markov equivalent to some AMP chain graph under marginalization of some of its nodes. However, MAMP chain graphs do not onl…
Elliptical slice sampling converges geometrically, providing reliable sampling for Bayesian learning.
Modified asymmetric hidden Markov models for time series with autoregressive components.
Continuous Hidden Markov Models for Equity Returns
Study reveals supply chain correlations in firm growth rates.
We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data. The conditional independence structure of an arbitrary distribution can be represented as an undirected graph (or Markov random field), but most algorithms for lea…
MCMC complexity matches optimization for large and .
New method estimates hidden binary mixture model centers efficiently.
We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables,…
This article considers a model for alternative processes for securities prices and compares this model with actual return data of several securities. The distributions of returns that appear in the model can be Gaussian as well as non-Gaussian; in particular they may have two peaks. We consider a discrete Markov chain …
We report an exact likelihood computation for Linear Gaussian Markov processes that is more scalable than existing algorithms for complex models and sparsely sampled signals. Better scaling is achieved through elimination of repeated computations in the Kalman likelihood, and by using the diagonalized form of the state…