Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models
Multivariate count data are defined as the number of items of different categories issued from sampling within a population, which individuals are grouped into categories. The analysis of multivariate count data is a recurrent and crucial issue in numerous modelling problems, particularly in the fields of biology and ecology (where the data can represent, for example, children counts associated with multitype branching processes), sociology and econometrics. We focus on I) Identifying categories that appear simultaneously, or on the contrary that are mutually exclusive. This is achieved by identifying conditional independence relationships between the variables; II)Building parsimonious parametric models consistent with these relationships; III) Characterising and testing the effects of covariates on the joint distribution of the counts. To achieve these goals, we propose an approach based on graphical probabilistic models, and more specifically partially directed acyclic graphs.
Code (0)
등록된 구현이 없습니다.
Tasks
EconometricsSociologySimilar Papers 제목 키워드 기반
The Multivariate Generalised von Mises distribution: Inference and applications
Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses thi…
Gaussian ProcessesWarped Dynamic Linear Models for Time Series of Counts
Dynamic Linear Models (DLMs) are commonly employed for time series analysis due to their versatile structure, simple recursive updating, ability to handle missing data, and probabilistic forecasting. However, the options…
Time SeriesTime Series AnalysisvalidDeep Learning of Multivariate Extremes via a Geometric Representation
The study of geometric extremes, where extremal dependence properties are inferred from the deterministic limiting shapes of scaled sample clouds, provides an exciting approach to modelling the extremes of multivariate d…
Deep LearningNormalizing Kalman Filters for Multivariate Time Series Analysis
This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting. To this extent, we present a novel approach reconciling classical state space models with deep learning m…
Deep LearningState Space ModelsTime SeriesTime Series AnalysisDecoupling multivariate functions using a non-parametric Filtered CPD approach
Black-box model structures are dominated by large multivariate functions. Usually a generic basis function expansion is used, e.g. a polynomial basis, and the parameters of the function are tuned given the data. This is …