10 citations · 21 across the 13 of their papers we have counts for
13 papers
Bayesian Posterior Learning of Mixed Graphical Models
Erdong Guo, Alexandros Beskos, Maria De Iorio
Mixed Graphical Models (MGMs) provide a flexible framework for structure learning from heterogeneous data by treating sets of both continuous and discrete. Bayesian inference for M…
Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics
Erdong Guo, Paul Jackson, Jin Min Yang +1
High-energy physics phenomenology often requires linking multiple computational tools to evaluate observables, likelihoods, and experimental constraints across nontrivial parameter…
A Unified Framework for Cluster Methods with Tensor Networks
Erdong Guo, David Draper
Markov Chain Monte Carlo (MCMC), and Tensor Networks (TN) are two powerful frameworks for numerically investigating many-body systems, each offering distinct advantages. MCMC, with…
Tensor network Monte Carlo simulations for the two-dimensional random-bond Ising model
Tao Chen, Erdong Guo, Wanzhou Zhang +2
Disordered lattice spin systems are crucial in both theoretical and applied physics. However, understanding their properties poses significant challenges for Monte Carlo simulation…
The Fundamental Patterns of Sea Surface Temperature
J. Xavier Prochaska, Erdong Guo, Peter C. Cornillon +1
For over 40 years, remote sensing observations of the Earth's oceans have yielded global measurements of sea surface temperature (SST). With a resolution of approximately 1km, thes…
Annealing Double-Head: An Architecture for Online Calibration of Deep Neural Networks
Erdong Guo, David Draper, Maria De Iorio
Model calibration, which is concerned with how frequently the model predicts correctly, not only plays a vital part in statistical model design, but also has substantial practical…
Discussion of Martingale Posterior Distributions by E. Fong, C. Holmes, and S. G. Walker
David Draper, Erdong Guo
In this discussion note, we respond to the fascinating paper "Martingale Posterior Distributions" by E. Fong, C. Holmes, and S. G. Walker with a couple of comments. On the basis of…
Neural Tangent Kernel of Matrix Product States: Convergence and Applications
Erdong Guo, David Draper
In this work, we study the Neural Tangent Kernel (NTK) of Matrix Product States (MPS) and the convergence of its NTK in the infinite bond dimensional limit. We prove that the NTK o…
A Simple Necessary Condition For Independence of Real-Valued Random Variables
David Draper, Erdong Guo, Robert Lund +1
The standard method to check for the independence of two real-valued random variables -- demonstrating that the bivariate joint distribution factors into the product of its margina…
The Practical Scope of the Central Limit Theorem
David Draper, Erdong Guo
The \textit{Central Limit Theorem (CLT)} is at the heart of a great deal of applied problem-solving in statistics and data science, but the theorem is silent on an important implem…
Representation Theorem for Matrix Product States
Erdong Guo, David Draper
In this work, we investigate the universal representation capacity of the Matrix Product States (MPS) from the perspective of boolean functions and continuous functions. We show th…
Infinitely Wide Tensor Networks as Gaussian Process
Erdong Guo, David Draper
Gaussian Process is a non-parametric prior which can be understood as a distribution on the function space intuitively. It is known that by introducing appropriate prior to the wei…
The Bayesian Method of Tensor Networks
Erdong Guo, David Draper
Bayesian learning is a powerful learning framework which combines the external information of the data (background information) with the internal information (training data) in a l…