Frontiers

Numerical foundations for scalable statistical and machine learning methods

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

Hidden geometry explains why kernel methods separate complex data so well

Are two sets of data genuinely different, or is it because of randomness? This question, known as the two-sample testing problem, becomes notoriously difficult in modern datasets, because they are ...

WiMi Explores Multi-Dimensional Pooling Optimization Technology under the Variational Quantum Algorithm Framework

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, is exploring multi-dimensional pooling optimization ...

Predictive surrogates could cut quantum computing measurement overhead by more than 99.97%

Quantum computers, systems that process information leveraging quantum mechanical effects, have the potential of ...
GitHub

Bayesian machine learning notebooks

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.
PNAS

Quantum advantage in variational Bayes inference

Quantum machine learning (QML) is an emerging research field that deals with quantum algorithms for data analysis. It is hoped that QML will yield practical demonstrations of quantum advantage by ...
IEEE

Distributed Variational Inference for Online Supervised Learning

Abstract: This article introduces a scalable distributed probabilistic inference algorithm for intelligent sensor networks, tackling challenges of continuous variables, intractable posteriors, and ...
IEEE

Variational Bayesian Inference Algorithms for Infinite Relational Model of Network Data

Abstract: Network data show the relationship among one kind of objects, such as social networks and hyperlinks on the Web. Many statistical models have been proposed for analyzing these data. For ...
GitHub

PyVBMC: Variational Bayesian Monte Carlo in Python

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference, previously implemented in MATLAB. VBMC is an approximate inference method ...
Scientific Research Publishing

Statistical Inversion Based on Nonlinear Weighted Anisotropic Total Variational Model and Its Application in Electrical Impedance Tomography ()

School of Mathematics and Statistics, Shandong Normal University, Jinan, China. Electrical impedance tomography (EIT) [1] is an imaging modality that aims to reconstruct the conductivity distribution ...
PNAS

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior

We propose an approach for joint trajectory analysis of multiple single-cell sequencing data, combining Bayesian hierarchical models with variational autoencoders. Based on a coherent statistical ...