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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…

Machine Learning · Statistics 2026-04-21 Giosue Migliorini , Padhraic Smyth

We propose a restrictiveness measure for economic models based on how well they fit synthetic data from a pre-defined class. This measure, together with a measure for how well the model fits real data, outlines a Pareto frontier, where…

Theoretical Economics · Economics 2023-08-25 Drew Fudenberg , Wayne Gao , Annie Liang

Multi-type Markov point processes offer a flexible framework for modelling complex multi-type point patterns where it is pertinent to capture both interactions between points as well as large scale trends depending on observed covariates.…

Methodology · Statistics 2025-10-15 Ib Thorsgaard Jensen , Jean-François Coeurjolly , Rasmus Waagepetersen

Latent feature modeling allows capturing the latent structure responsible for generating the observed properties of a set of objects. It is often used to make predictions either for new values of interest or missing information in the…

Machine Learning · Statistics 2018-03-09 Isabel Valera , Melanie F. Pradier , Maria Lomeli , Zoubin Ghahramani

We propose a new flexible tensor model for multiple-equation regression that accounts for latent regime changes. The model allows for dynamic coefficients and multi-dimensional covariates that vary across equations. We assume the…

Methodology · Statistics 2024-07-02 Roberto Casarin , Radu Craiu , Qing Wang

The latent feature relational model (LFRM) is a generative model for graph-structured data to learn a binary vector representation for each node in the graph. The binary vector denotes the node's membership in one or more communities. At…

Machine Learning · Statistics 2018-07-11 Gundeep Arora , Anupreet Porwal , Kanupriya Agarwal , Avani Samdariya , Piyush Rai

Pattern-mixture models provide a transparent approach for handling missing data, where the full-data distribution is factorized in a way that explicitly shows the parts that can be estimated from observed data alone, and the parts that…

Methodology · Statistics 2019-04-26 Yen-Chi Chen , Mauricio Sadinle

The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we extend the Markov-modulated Poisson process framework through…

Risk Management · Quantitative Finance 2020-08-06 Benjamin Avanzi , Greg Taylor , Bernard Wong , Alan Xian

Latent variable models provide a powerful framework for incorporating and inferring unobserved factors in observational data. In causal inference, they help account for hidden factors influencing treatment or outcome, thereby addressing…

Machine Learning · Computer Science 2025-08-29 Tetsuro Morimura , Tatsushi Oka , Yugo Suzuki , Daisuke Moriwaki

This paper presents robust inference methods for general linear hypotheses in linear panel data models with latent group structure in the coefficients. We employ a selective conditional inference approach, deriving the conditional…

Econometrics · Economics 2025-11-25 Oguzhan Akgun , Ryo Okui

We provide a comprehensive overview of latent Markov (LM) models for the analysis of longitudinal categorical data. The main assumption behind these models is that the response variables are conditionally independent given a latent process…

Statistics Theory · Mathematics 2010-03-16 F. Bartolucci , A. Farcomeni , F. Pennoni

This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or…

Machine Learning · Statistics 2017-07-27 Isabel Valera , Melanie F. Pradier , Zoubin Ghahramani

Irregular multivariate time series impose a trade-off for long-horizon forecasting: discrete methods can distort temporal structure via re-gridding, while continuous-time models often require sequential solvers prone to drift. To bridge…

Machine Learning · Computer Science 2026-05-20 Zinuo You , Jin Zheng , John Cartlidge

In recent years, diffusion models, and more generally score-based deep generative models, have achieved remarkable success in various applications, including image and audio generation. In this paper, we view diffusion models as an implicit…

Statistics Theory · Mathematics 2026-02-12 Hyeok Kyu Kwon , Dongha Kim , Ilsang Ohn , Minwoo Chae

Discrete diffusion models have emerged as a powerful class of models and a promising route to fast language generation, but practical implementations typically rely on factored reverse transitions ignoring cross-token dependencies and…

Machine Learning · Computer Science 2026-05-14 Dario Shariatian , Alain Durmus , Umut Simsekli , Stefano Peluchetti

Graphical models are a rich language for describing high-dimensional distributions in terms of their dependence structure. While there are algorithms with provable guarantees for learning undirected graphical models in a variety of…

Machine Learning · Computer Science 2018-11-07 Guy Bresler , Frederic Koehler , Ankur Moitra , Elchanan Mossel

Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model…

Machine Learning · Computer Science 2016-07-04 Fariba Yousefi , Zhenwen Dai , Carl Henrik Ek , Neil Lawrence

Latent dynamical models are commonly used to learn the distribution of a latent dynamical process that represents a sequence of noisy data samples. However, producing samples from such models with high fidelity is challenging due to the…

Machine Learning · Computer Science 2023-08-17 Mohammad R. Rezaei

This paper presents a machine learning driven framework for sectoral stress testing in the Indian financial market, focusing on financial services, information technology, energy, consumer goods, and pharmaceuticals. Initially, we address…

Risk Management · Quantitative Finance 2025-07-04 Vidya Sagar G , Shifat Ali , Siddhartha P. Chakrabarty

Nonparametric models are versatile, albeit computationally expensive, tool for modeling mixture models. In this paper, we introduce spectral methods for the two most popular nonparametric models: the Indian Buffet Process (IBP) and the…

Machine Learning · Computer Science 2017-04-04 Hsiao-Yu Fish Tung , Chao-Yuan Wu , Manzil Zaheer , Alexander J. Smola