Trending Research
$R^*$: A robust MCMC convergence diagnostic with uncertainty using gradient-boosted machines
Markov chain Monte Carlo (MCMC) has transformed Bayesian model inference over the past three decades and is now a workhorse of applied scientists.
Methodology Applications
Computing Extremely Accurate Quantiles Using t-Digests
We present on-line algorithms for computing approximations of rank-based statistics that give high accuracy, particularly near the tails of a distribution, with very small sketches.
Sequential Quantile Estimation
Computation
Data Structures and Algorithms
Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC
In this paper we show that the convergence diagnostic $\widehat{R}$ of Gelman and Rubin (1992) has serious flaws.
Computation Methodology
ruptures: change point detection in Python
ruptures is a Python library for offline change point detection.
Computation Mathematical Software
collapse: Advanced and Fast Statistical Computing and Data Transformation in R
collapse is a large C/C++-based infrastructure package facilitating complex statistical computing, data transformation, and exploration tasks in R - at outstanding levels of performance and memory efficiency.
Computation
The Building Data Genome Project 2: Hourly energy meter data from the ASHRAE Great Energy Predictor III competition
This paper describes an open data set of 3, 053 energy meters from 1, 636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17, 544 measurements per meter resulting in approximately 53. 6 million measurements).
Applications
CVXR: An R Package for Disciplined Convex Optimization
CVXR is an R package that provides an object-oriented modeling language for convex optimization, similar to CVX, CVXPY, YALMIP, and Convex. jl.
Computation
PyDMD: A Python package for robust dynamic mode decomposition
The method's linear algebra-based formulation additionally allows for a variety of optimizations and extensions that make the algorithm practical and viable for real-world data analysis.
Computation Systems and Control Systems and Control Dynamical Systems Computational Physics
Flexible hierarchical risk modeling for large insurance data via NumPyro
Data analysis and individual policy-level modeling for insurance involves handling large data sets with strong spatiotemporal correlations, non-Gaussian distributions, and complex hierarchical structures.
Applications
Co-Active Subspace Methods for the Joint Analysis of Adjacent Computer Models
Active subspace (AS) methods are a valuable tool for understanding the relationship between the inputs and outputs of a Physics simulation.
Methodology
