Non-stationary count time series characterized by features such as abrup...
Ridge detection is a classical tool to extract curvilinear features in i...
Non-fungible tokens (NFT) have recently emerged as a novel blockchain ho...
We propose a flexible approach for the detection of features in images w...
We present an approach to clustering time series data using a model-base...
We introduce a new version of deep state-space models (DSSMs) that combi...
We present Bayesian Spillover Graphs (BSG), a novel method for learning
...
Lightning is a destructive and highly visible product of severe storms, ...
A functional time series approach is proposed for investigating spatial
...
The U.S. electrical grid has undergone substantial transformation with
i...
Classification of large multivariate time series with strong class imbal...
In this article, we introduce mixture representations for likelihood rat...
High-dimensional time series datasets are becoming increasingly common i...
Changepoint models enjoy a wide appeal in a variety of disciplines to mo...
We examine the use of time series data, derived from Electric Cell-subst...
This paper proposes novel methods to test for simultaneous diagonalizati...
The electric power grid is a critical societal resource connecting multi...
Independent component analysis (ICA) is an unsupervised learning method
...
A Vector Auto-Regressive (VAR) model is commonly used to model multivari...
We introduce global-local shrinkage priors into a Bayesian dynamic linea...
We make a simple observation that facilitates valid likelihood-based
inf...
We explore the role of Conditional Generative Adversarial Networks (GAN)...
Denoising is a fundamental challenge in scientific imaging. Deep
convolu...
Measurements of many biological processes are characterized by an initia...
Despite significant advances, continual learning models still suffer fro...
Anomaly detection aims to identify observations that deviate from the ty...
As a crucial problem in statistics is to decide whether additional varia...
We apply both distance-based (Jin and Matteson, 2017) and kernel-based
(...
Independent component analysis (ICA) decomposes multivariate data into
m...
The Vector AutoRegressive (VAR) model is fundamental to the study of
mul...
We consider cell line classification using multivariate time series data...
Vector autoregression (VAR) is a fundamental tool for modeling the joint...